Data Mining Classification and Prediction by Dr. Tanvir Ahmed

7/24/2019 Data Mining Classification and Prediction by Dr. Tanvir Ahmed

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November 16, 2015Data Mining: Concepts and

Techniques 1

Classifcation and

Prediction


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Techniques 2

Classifcation and Prediction

hat is c!assi"cation# hatis prediction#

$ssues regarding

c!assi"cation and prediction

C!assi"cation b% decision

tree induction

&a%esian c!assi"cation

'a(% !earners )or !earning*rom %our neighbors+

ther c!assi"cation

methods

-rediction

.ccurac% and error

measures

/nsemb!e methods

Mode! se!ection

ummar%


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Techniques

C!assi"cation predicts categorica! c!ass !abe!s )discrete or nomina!+ c!assi"es data )constructs a mode!+ based on the

training set and the va!ues )c!ass !abe!s+ in ac!assi*%ing attribute and uses it in c!assi*%ing nedata

-rediction mode!s continuous3va!ued *unctions, i4e4, predicts

unnon or missing va!ues

T%pica! app!ications Credit approva! Target mareting Medica! diagnosis raud detection

Classifcation vs. Prediction


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Techniques 7

ClassifcationA Two-StepProcess

Mode! construction: describing a set o* predetermined c!asses /ach tup!e8samp!e is assumed to be!ong to a prede"ned

c!ass, as determined b% the c!ass !abe! attribute The set o* tup!es used *or mode! construction is training

set

The mode! is represented as c!assi"cation ru!es, decisiontrees, or mathematica! *ormu!ae Mode! usage: *or c!assi*%ing *uture or unnon ob9ects

/stimate accurac%o* the mode! The non !abe! o* test samp!e is compared ith the

c!assi"ed resu!t *rom the mode! .ccurac% rate is the percentage o* test set samp!es

that are correct!% c!assi"ed b% the mode! Test set is independent o* training set, otherise over3

"tting i!! occur $* the accurac% is acceptab!e, use the mode! to c!assi*%

datatup!es hose c!ass !abe!s are not non


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Techniques 5

rocess : o eConstruction

Training

Data

NAME RANK YEARS TENURED

Mike Assistant Prof 3 no

Mary Assistant Prof 7 yesBill Professor 2 yes

Jim Associate Prof 7 yes

Dave Assistant Prof 6 no

Anne Associate Prof 3 no

Classification

Algorithms

IF rank = professor

OR years > 6

T!" ten#re$ = yes

Classifier

%&o$el'


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Techniques 6

Process (2): sin! t"e Model in

Prediction

Classifier

Testing

Data

NAME RANK YEARS TENURED

Tom Assistant Prof 2 no

Merlisa Associate Prof 7 no

George Professor 5 yes

Josep Assistant Prof 7 yes

(nseen Data

%)eff* +rofessor* ,'

Ten#re$-


7/124November 16, 2015Data Mining: Concepts and

Techniques

Supervised vs. nsupervised#earnin!

upervised !earning )c!assi"cation+

upervision: The training data )observations,

measurements, etc4+ are accompanied b%

!abe!s indicating the c!ass o* the observations Ne data is c!assi"ed based on the training set

;nsupervised !earning)c!ustering+

The c!ass !abe!s o* training data is unnon



Techniques >

C"apter $. Classifcation andPrediction


$ssues regarding



tree induction


upport ?ector Machines

)?M+

'a(% !earners )or !earning*rom %our neighbors+

ther c!assi"cation

methods

-rediction

.ccurac% and error

measures

/nsemb!e methods

Mode! se!ection

ummar%



Techniques @

%ssues: &ata Preparation

Data c!eaning

-reprocess data in order to reduce noise and

hand!e missing va!ues

Ae!evance ana!%sis )*eature se!ection+ Aemove the irre!evant or redundant attributes

Data trans*ormation



Techniques 10

%ssues: 'valuatin! Classifcation

Met"ods

.ccurac% c!assi"er accurac%: predicting c!ass !abe! predictor accurac%: guessing va!ue o* predicted

attributes peed

time to construct the mode! )training time+ time to use the mode! )c!assi"cation8prediction time+

Aobustness: hand!ing noise and missing va!ues ca!abi!it%: eBcienc% in dis3resident databases $nterpretabi!it%

understanding and insight provided b% the mode! ther measures, e4g4, goodness o* ru!es, such as

decision tree si(e or compactness o* c!assi"cation ru!es



Techniques 11



$ssues regarding



tree induction


Au!e3based c!assi"cation

C!assi"cation b% bac

propagation

upport ?ector Machines)?M+

.ssociative c!assi"cation

'a(% !earners )or !earning *rom

%our neighbors+

ther c!assi"cation methods

-rediction

.ccurac% and error measures

/nsemb!e methods

Mode! se!ection

ummar%

& i i T % d i T i i



Techniques 12

&ecision Tree %nduction: Trainin!&ataset

age income st!"ent cre"it#rating $!ys#comp!ter

%&3' ig no fair no

%&3' ig no e(cellent no

3)*+' ig no fair yes

,+' me"i!m no fair yes

,+' lo- yes fair yes

,+' lo- yes e(cellent no

3)*+' lo- yes e(cellent yes

%&3' me"i!m no fair no

%&3' lo- yes fair yes

,+' me"i!m yes fair yes

%&3' me"i!m yes e(cellent yes

3)*+' me"i!m no e(cellent yes

3)*+' ig yes fair yes

,+' me"i!m no e(cellent no

This*o!!os

ane=amp!eo*uin!ans$D)-!a%ing

Tennis+



Techniques 1

utput: A &ecision Tree or*buys_computer

age-

o.ercast

st#$ent- cre$it rating-

40

no yes yes

yes

31..40

no

faire/cellentyesno



Techniques 17

%nduction

&asic a!gorithm )a greed% a!gorithm+ Tree is constructed in a top3don recursive divide3and3

conquer manner .t start, a!! the training e=amp!es are at the root .ttributes are categorica! )i* continuous3va!ued, the% are

discreti(ed in advance+ /=amp!es are partitioned recursive!% based on se!ected

attributes Test attributes are se!ected on the basis o* a heuristic or

statistica! measure )e4g4, in*ormation gain+

Conditions *or stopping partitioning .!! samp!es *or a given node be!ong to the same c!ass There are no remaining attributes *or *urther partitioning E

ma9orit% votingis emp!o%ed *or c!assi*%ing the !ea*

There are no samp!es !e*t


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," &ecision tree is popular

Does not require domain no!edge Can hand!e mu!tidimensiona! data /as% to understand



Techniques 16

Attri/ute Selection Measure:%nor+ation 0ain (%&C3.4)

e!ect the attribute ith the highest in*ormationgain4 'east impurit%4

'etpibe the probabi!it% that an arbitrar% tup!e in D

be!ongs to c!ass Ci, estimated b% FCi, DF8FDF

/=pected in*ormation)entrop%+ needed to c!assi*%a tup!e in D:

$n*ormationneeded )a*ter using . to sp!it D into vpartitions+ to c!assi*% D:

$n*ormation gainedb% branching on attribute .

'%log'% 01

i

m

i

i ppDInfo =

=

'%22

22'%

1

j

v

j

j

A DID

DDInfo =

=

(D)InfoInfo(D)Gain(A) A=


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Data Mining: Concepts andTechniques 1

Attri/ute Selection: %nor+ation 0ain

C!ass -: bu%sGcomputer HI%esJ C!ass N: bu%sGcomputer H

InoJ

means Iage KH0J has

5 out o* 17 samp!es, ith 2

%eses and nos4 Lence

imi!ar!%,

63,45'0*%1,

7

'5*,%1,

,'*0%1,

7'%

=+

+=

I

IIDInfoage

5,845'9%

17145'%

50345'%

===

ratingcreditGain

studentGain

incomeGain

0,645'%'%'% == DInfoDInfoageGain age

'*0%1,

7I

3,545'1,

7%log

1,

7'

1,

3%log

1,

3'7*3%'% 00 === IDInfo


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Data Mining: Concepts andTechniques 1>

Co+putin! %nor+ation-0ain orContinuous-5alue Attri/utes

'et attribute . be a continuous3va!ued attribute Must determine the best split point*or .

ort the va!ue . in increasing order

T%pica!!%, the midpoint beteen each pair o* ad9acent

va!ues is considered as a possib!e split point )aiai1+82 is the midpoint beteen the va!ues o* a iand ai1

The point ith the minimum expected information

requirement*or . is se!ected as the sp!it3point *or . p!it:

D1 is the set o* tup!es in D satis*%ing . sp!it3point,

and D2 is the set o* tup!es in D satis*%ing . O sp!it3

point

0 i 6 ti Att i/ t S l ti


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Data Mining: Concepts andTechniques 1@

0ain 6atio or Attri/ute Selection(C3.4)

$n*ormation gain measure is biased toardsattributes ith a !arge number o* va!ues

C745 )a successor o* $D+ uses gain ratio to overcome

the prob!em )norma!i(ation to in*ormation gain+


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0ini inde7 (CA6T8 %9M%ntelli!entMiner)

$* a data set D contains e=amp!es *rom nc!asses, gini inde=,gini)D+ is de"ned as

herepjis the re!ative *requenc% o* c!assjin D

$* a data set D is sp!it on . into to subsets D1and D2, the giniinde= gini)D+ is de"ned as

Aeduction in $mpurit%:

The attribute provides the sma!!est ginisplit)D+ )or the !argest

reduction in impurit%+ is chosen to sp!it the node )need to

enumerate all the possible splitting points for each attribute+

=

=n

j

p jDgini

1

01'%

'%22

22'%

22

22'% 0

01

1Dgini

D

DDgini

D

DDginiA +=

'%'%'% DginiDginiAginiA

=

0ini inde7 (CA6T %9M


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0ini inde7 (CA6T8 %9M%ntelli!entMiner)

/=4 D has @ tup!es in bu%sGcomputer H I%esJ and 5 in InoJ

uppose the attribute income partitions D into 10 in D1: P!o,

mediumQ and 7 in D2

but giniPmedium,highQis 040 and thus the best since it is the !oest

.!! attributes are assumed continuous3va!ued

Ma% need other too!s, e4g4, c!ustering, to get the possib!e sp!it

va!ues

Can be modi"ed *or categorica! attributes

,73451,

7

1,

31'%

00

=

=Dgini

'%1,

,

'%1,

15

'% 11:*; DGiniDGiniDgini mediumlowincome

+

=


22/124November 16, 2015


Co+parin! Attri/ute SelectionMeasures

The three measures, in genera!, return good resu!tsbut

$n*ormation gain:

biased toards mu!tiva!ued attributes


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Techniques 2

t"er Attri/ute SelectionMeasures

CL.$D: a popu!ar decision tree a!gorithm, measure based on S2

test *orindependence

C3/-: per*orms better than in*o4 gain and gini inde= in certain cases


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Techniques 27

verfttin! and Tree Prunin!

ver"tting: .n induced tree ma% over"t the training data Too man% branches, some ma% reect anoma!ies due to noise or

out!iers

-oor accurac% *or unseen samp!es

To approaches to avoid over"tting -repruning: La!t tree construction ear!%Udo not sp!it a node i* this

ou!d resu!t in the goodness measure *a!!ing be!o a thresho!d

DiBcu!t to choose an appropriate thresho!d

-ostpruning: Aemove branches *rom a I*u!!% gronJ treeUget a

sequence o* progressive!% pruned trees

;se a set o* data diVerent *rom the training data to decide

hich is the Ibest pruned treeJ


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Techniques 25

'n"ance+ents to 9asic &ecision Tree%nduction

.!!o *or continuous3va!ued attributes D%namica!!% de"ne ne discrete3va!ued attributes

that partition the continuous attribute va!ue into adiscrete set o* interva!s

Land!e missing attribute va!ues .ssign the most common va!ue o* the attribute

.ssign probabi!it% to each o* the possib!e va!ues

.ttribute construction Create ne attributes based on e=isting ones that

are sparse!% represented

This reduces *ragmentation, repetition, and

rep!ication


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Techniques 26

Classifcation in #ar!e &ata/ases

C!assi"cationUa c!assica! prob!em e=tensive!% studiedb% statisticians and machine !earning researchers

ca!abi!it%: C!assi*%ing data sets ith mi!!ions o*

e=amp!es and hundreds o* attributes ith reasonab!e

speed h% decision tree induction in data mining#

re!ative!% *aster !earning speed )than otherc!assi"cation methods+

convertib!e to simp!e and eas% to understandc!assi"cation ru!es can use ' queries *or accessing databases comparab!e c!assi"cation accurac% ith other

methods

Scala/le &ecision Tree %nduction


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Techniques 2

Scala/le &ecision Tree %nductionMet"ods

'$)/D&T@6 U Mehta et a!4+ &ui!ds an inde= *or each attribute and on!% c!ass !ist

and the current attribute !ist reside in memor% -A$NT)?'D&@6 U W4 ha*er et a!4+

Constructs an attribute !ist data structure -;&'$C)?'D&@> U Aastogi X him+

$ntegrates tree sp!itting and tree pruning: stopgroing the tree ear!ier

Aainorest )?'D&@> U


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Techniques 2>

ca a t ra+ewor or6ainorest

.eparates te scala$ility aspects from te criteria tat"etermine te /!ality of te tree

B!il"s an A01list: AVC (Attribute, Value, Class_label)

AVC-set of an attri$!teX4 Pro5ection of training "ataset onto te attri$!teXan"

class la$el -ere co!nts of in"ivi"!al class la$el are

aggregate" AVC-grou of a no"e n4

.et of A01sets of all pre"ictor attri$!tes at te no"e n

6ainorest: Trainin! Set and %ts A5C


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Techniques 2@

6ainorest: Trainin! Set and %ts A5CSets

st!"ent B!y#1omp!ter

yes no

yes 6 )

no 3 +

Age B!y#1omp!ter

yes no

%&3' 3 2

3)+' + '

,+' 3 2

1re"itrating

B!y#1omp!ter

yes no

fair 6 2

e(cellent 3 3

.?C3set on incom.?C3set onAge

.?C3set on Student

Training /=amp!esincome B!y#1omp!ter

yes no

ig 2 2

me"i!m + 2

lo- 3 )

.?C3set oncredit_rating


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Techniques 0

&ata Cu/e-9ased &ecision-Tree%nduction

$ntegration o* genera!i(ation ith decision3tree

induction )Yamber et a!4@+

C!assi"cation at primitive concept !eve!s

/4g4, precise temperature, humidit%, out!oo, etc4 'o3!eve! concepts, scattered c!asses, bush%

c!assi"cation3trees

emantic interpretation prob!ems Cube3based mu!ti3!eve! c!assi"cation

Ae!evance ana!%sis at mu!ti3!eve!s

$n*ormation3gain ana!%sis ith dimension !eve!


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Techniques 1

9AT (9ootstrapped pti+isticAl!orit"+ or Tree Construction)

7se a statistical tecni/!e calle" bootstrappingto create

several smaller samples s!$sets48 eac fits in memory

9ac s!$set is !se" to create a tree8 res!lting in several

trees Tese trees are e(amine" an" !se" to constr!ct a ne-

tree T

:t t!rns o!t tatTis very close to te tree tat -o!l"$e generate" !sing te -ole "ata set togeter

A"v; re/!ires only t-o scans of DB8 an incremental alg

P t ti Cl if ti 6 lt


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Techniques 2

Presentation o Classifcation 6esults


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Techniques

5isuali


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Techniques 7

%nteractive 5isual Minin!/ Perception-

9ased Classifcation (P9C)


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Techniques 5



$ssues regarding



tree induction




propagation




%our neighbors+


-rediction

.ccurac% and error measures /nsemb!e methods

Mode! se!ection

ummar%

9 i Cl if ti


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Techniques 6

9aesian Classifcation:,"

. statistica! c!assi"er: per*ormsprobabilisticprediction, i.e.,predicts c!ass membershipprobabi!ities

oundation: &ased on &a%es Theorem4 -er*ormance: . simp!e &a%esian c!assi"er, nae

!a"esian classi#er, has comparab!e per*ormance ithdecision tree and se!ected neura! netor c!assi"ers

$ncrementa!: /ach training e=amp!e can incrementa!!%increase8decrease the probabi!it% that a h%pothesis iscorrect U prior no!edge can be combined ith

observed data tandard: /ven hen &a%esian methods are

computationa!!% intractab!e, the% can provide astandard o* optima! decision maing against hichother methods can be measured


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Techniques

9aesian T"eore+: 9asics

'et >be a data samp!e )IeidenceJ+: c!ass !abe! isunnon

'et L be a h"pothesisthat Z be!ongs to c!ass C

C!assi"cation is to determine -)LF>+, the probabi!it% that

the h%pothesis ho!ds given the observed data samp!e > -)L+ )prior probabilit"+, the initia! probabi!it%

/4g4,>i!! bu% computer, regard!ess o* age, income, [

-)>+: probabi!it% that samp!e data is observed

-)>FL+ )posteriori probabilit"+, the probabi!it% o* observingthe samp!e >, given that the h%pothesis ho!ds

/4g4,i!! bu% computer, the prob4 that Z is

14470, medium income


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Techniques >

9aesian T"eore+

, posteriori probabilit" of ah"pothesis L, -)LF>+, *o!!os the &a%es theorem

$n*orma!!%, this can be ritten as

posteriori H !ie!ihood = prior8evidence

-redicts >be!ongs to C2iV the probabi!it% -)CiF>+ is

the highest among a!! the -)CFZ+ *or a!! the $c!asses

-ractica! diBcu!t%: require initia! no!edge o* man%

probabi!ities, signi"cant computationa! cost

'%'%'2%'2%

X

XX

PHPHPHP =

owar s a ve aes an


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Techniques @

owar s a ve aes anClassifer

'et D be a training set o* tup!es and their associatedc!ass !abe!s, and each tup!e is represented b% an n3Dattribute vector >H )=1, =2, [, =n+

uppose there are mc!asses C1, C2, [, Cm4

C!assi"cation is to derive the ma=imum posteriori,i4e4, the ma=ima! -)CiF>+ This can be derived *rom &a%es theorem

ince -)Z+ is constant *or a!! c!asses, on!%

needs to be ma=imi(ed

'%

'%'2%'2%

X

XX

Pi

CPi

CP

iCP =

'%'2%'2%i

CPi

CPi

CP XX =

er va on o a ve aes


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Techniques 70

er va on o a ve aesClassifer

. simp!i"ed assumption: attributes are conditiona!!%independent )i4e4, no dependence re!ation beteenattributes+:

This great!% reduces the computation cost: n!%

counts the c!ass distribution $* .is categorica!, -)=FCi+ is the R o* tup!es in Ci

having va!ue =*or .divided b% FCi, DF )R o* tup!es o*Ciin D+

$* .is continous3va!ued, -)=FCi+ is usua!!% computedbased on


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Techniques 71

?a ve 9aes an ass er: Tra n n!&ataset

C!ass:

C1:bu%sGcomputer H

^%es

C2:bu%sGcomputer H ^no

Data samp!e

Z H )age KH0,

$ncome H medium,

tudent H %esCreditGrating H air+


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Techniques 72

'7a+ple

-)Ci+: -)bu%sGcomputer H I%esJ+ H @817 H 0467 -)bu%sGcomputer H InoJ+ H 5817H 045

Compute -)ZFCi+ *or each c!ass -)age H IKH0J F bu%sGcomputer H I%esJ+ H 28@ H 04222 -)age H IKH 0J F bu%sGcomputer H InoJ+ H 85 H 046 -)income H ImediumJ F bu%sGcomputer H I%esJ+ H 78@ H 04777

-)income H ImediumJ F bu%sGcomputer H InoJ+ H 285 H 047 -)student H I%esJ F bu%sGcomputer H I%es+ H 68@ H 0466 -)student H I%esJ F bu%sGcomputer H InoJ+ H 185 H 042 -)creditGrating H I*airJ F bu%sGcomputer H I%esJ+ H 68@ H 0466 -)creditGrating H I*airJ F bu%sGcomputer H InoJ+ H 285 H 047

> (a!e B = 8 inco+e +ediu+8 student es8 creditratin! air)

P(>DCi) :-)ZFbu%sGcomputer H I%esJ+ H 04222 = 04777 = 0466 = 0466 H 04077 -)ZFbu%sGcomputer H InoJ+ H 046 = 047 = 042 = 047 H 0401@P(>DCi)EP(Ci) : -)ZFbu%sGcomputer H I%esJ+ _ -)bu%sGcomputer H I%esJ+ H 0402>

-)ZFbu%sGcomputer H InoJ+ _ -)bu%sGcomputer H InoJ+ H 0400

T"ereore8 > /elon!s to class (*/usco+puter esF)

vo n! e - ro a


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Techniques 7

vo n! e ro a Pro/le+

Na`ve &a%esian prediction requires each conditiona! prob4 benon3(ero4 therise, the predicted prob4 i!! be (ero

/=4 uppose a dataset ith 1000 tup!es, incomeH!o )0+,incomeH medium )@@0+, and income H high )10+,

;se 'ap!acian correction )or 'ap!acian estimator+ .dding 1 to each case

-rob)income H !o+ H 18100-rob)income H medium+ H @@18100

-rob)income H high+ H 118100 The IcorrectedJ prob4 estimates are c!ose to their

IuncorrectedJ counterparts

=

=n

kCixkPCiP

1

'2%'2%

a ve aes an ass er:


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Techniques 77

a ve aes an ass er:Co++ents

.dvantages /as% to imp!ement


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Techniques 75

9aesian 9elie ?etwor;s

&a%esian be!ie* netor a!!os a subseto* the

variab!es conditiona!!% independent

. graphica! mode! o* causa! re!ationships

Aepresents dependenc% among the variab!es


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Techniques 76

'7a+ple

FamilyHistory

LungCancer

PositiveXRay

Smoer

!m"#ysema

$ys"nea

LC

%LC

&FH' S) &FH' %S) &%FH' S) &%FH' %S)

0.*

0.+

0.,

0.,

0.-

0.3

0.1

0.

aesian 9elie ?etwor;s

The conditional pro/a/ilitta/le)CPT+ *or variab!e'ungCancer:

=

=n

i

!Parents ixiPxxP n1

''%2%'*444*% 1

C-T shos the conditiona! probabi!it%*or each possib!e combination o* itsparents

Derivation o* the probabi!it% o* aparticu!ar combination o* va!ueso* >, *rom C-T:


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Techniques 7

Trainin! 9aesian ?etwor;s

evera! scenarios:


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Techniques 7>



$ssues regarding



tree induction




propagation




%our neighbors+


-rediction


Mode! se!ection

ummar%


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Techniques 7@

sin! %-TI'? 6ules or Classifcation

Aepresent the no!edge in the *orm o* $3TL/Nru!es

A: $ ageH %outh .ND studentH %es TL/N bu"s_computerH %es

Au!e antecedent8precondition vs4 ru!e consequent

.ssessment o* a ru!e: coerageand accurac"

ncovers H R o* tup!es covered b% A

ncorrect H R o* tup!es correct!% c!assi"ed b% Acoverage)A+ H ncovers 8FDF 8_ D: training data set _8

accurac%)A+ H ncorrect 8 ncovers $* more than one ru!e is triggered, need conJict resolution

i(e ordering: assign the highest priorit% to the triggering ru!es that

has the ItoughestJ requirement )i4e4, ith the most attribute test+ C!ass3based ordering: decreasing order o*prealence or

misclassi#cation cost per class

Au!e3based ordering )decision list+: ru!es are organi(ed into one !ong

priorit% !ist, according to some measure o* ru!e qua!it% or b% e=perts


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Techniques 50

age-

st#$ent- cre$it rating-

40

no yes yes

yes

31..40

no

faire/cellentyesno

/=amp!e: Au!e e=traction *rom our bu"s_computerdecision3tree

$ ageH %oung .ND studentH no TL/N bu"s_computerH no

$ ageH %oung .ND studentH"es TL/N bu"s_computerH"es

$ ageH mid3age TL/N bu"s_computerH"es

$ ageH o!d .ND credit_ratingH excellent TL/N bu"s_computer H"es

$ ageH %oung .ND credit_ratingH fair TL/N bu"s_computerH no

6ule '7traction ro+ a &ecision Tree

Au!es are easier to understand than !argetrees

ne ru!e is created *or each path *rom the

root to a !ea*

/ach attribute3va!ue pair a!ong a path*orms a con9unction: the !ea* ho!ds the

c!ass prediction

Au!es are mutua!!% e=c!usive and

e=haustive

6u e '7tract on ro+ t e Tra n n!


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Techniques 51

u e ac o o e a !&ata

equentia! covering a!gorithm: /=tracts ru!es direct!% *rom trainingdata

T%pica! sequentia! covering a!gorithms: $', ., CN2, A$--/A

Au!es are !earned sequentiall", each *or a given c!ass Ci i!! cover

man% tup!es o* Ci but none )or *e+ o* the tup!es o* other c!asses

teps:

Au!es are !earned one at a time

/ach time a ru!e is !earned, the tup!es covered b% the ru!es are

removed

The process repeats on the remaining tup!es un!ess terminationcondition, e4g4, hen no more training e=amp!es or hen the

qua!it% o* a ru!e returned is be!o a user3speci"ed thresho!d

Comp4 4 decision3tree induction: !earning a set o* ru!es

simultaneousl"


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Techniques 52

Iow to #earn-ne-6ule

tar ith the most genera! ru!e possib!e: condition H empt%

.dding ne attributes b% adopting a greed% depth3"rst strateg%

-ics the one that most improves the ru!e qua!it%

Au!e3ua!it% measures: consider both coverage and accurac%

oi!3gain )in $' X A$--/A+: assesses in*oGgain b% e=tending

condition

$t *avors ru!es that have high accurac% and cover man% positive tup!es

Au!e pruning based on an independent set o* test tup!es

-os8neg are R o* positive8negative tup!es covered b% A4

$* )*+_&runeis higher *or the pruned version o* A, prune A

'log


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Techniques 5



$ssues regarding



tree induction




propagation


)?M+



%our neighbors+


-rediction


Mode! se!ection

ummar%

Classifcation: A Mat"e+atical


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Techniques 57

C!assi"cation: predicts categorica! c!ass !abe!s

/4g4, -ersona! homepage c!assi"cation

=iH )=1, =2, =, [+, %iH 1 or E1 =1: R o* a ord IhomepageJ

=2: R o* a ord Ie!comeJ

Mathematica!!% = Z H n, % H P1, E1Q e ant a *unction *: Z

Classifcation: A Mat"e+aticalMappin!


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Techniques 55

#inear Classifcation

&inar% C!assi"cationprob!em The data above the red

!ine be!ongs to c!ass ^=

The data be!o red !inebe!ongs to c!ass ^o /=amp!es: ?M,

-erceptron,

-robabi!istic C!assi"ers

=

==

=

==

=

=

=

= ooooo

o

o

o

o o

o

o

o

i i i i Cl if


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Techniques 56

&iscri+inative Classifers

.dvantages prediction accurac% is genera!!% high

.s compared to &a%esian methods E in genera!

robust, ors hen training e=amp!es contain errors

*ast eva!uation o* the !earned target *unction &a%esian netors are norma!!% s!o

Criticism !ong training time

diBcu!t to understand the !earned *unction )eights+ &a%esian netors can be used easi!% *or pattern discover%

not eas% to incorporate domain no!edge /as% in the *orm o* priors on the data or distributions


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Techniques 5

Perceptron K ,innow

?ector: =,

ca!ar: =, %,

$nput: P)=1, %1+, [Q

utput: c!assi"cation *unction*)=+

*)=i+ O 0 *or %iH 1

*)=i+ K 0 *or %iH 31

*)=+ HO = b H 0

or 1=12=2b H 0

=1

=2

-erceptron: update additive!%

inno: update mu!tip!icative!%

ass ca on


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Techniques 5>

9ac;propa!ation

&acpropagation: . neural networ; !earning a!gorithm tarted b% ps%cho!ogists and neurobio!ogists to deve!op

and test computationa! ana!ogues o* neurons

. neura! netor: . set o* connected input8output units

here each connection has a wei!"tassociated ith it During the !earning phase, the networ; learns /

adLustin! t"e wei!"tsso as to be ab!e to predict the

correct c!ass !abe! o* the input tup!es

.!so re*erred to as connectionist learnin!due to the

connections beteen units


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Techniques 5@

?eural ?etwor; as a Classifer

eaness 'ong training time Aequire a number o* parameters t%pica!!% best determined

empirica!!%, e4g4, the netor topo!og% or structure4 -oor interpretabi!it%: DiBcu!t to interpret the s%mbo!ic

meaning behind the !earned eights and o* hidden units inthe netor

trength Ligh to!erance to nois% data .bi!it% to c!assi*% untrained patterns

e!!3suited *or continuous3va!ued inputs and outputs uccess*u! on a ide arra% o* rea!3or!d data .!gorithms are inherent!% para!!e! Techniques have recent!% been deve!oped *or the e=traction o*

ru!es *rom trained neura! netors


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Techniques 60

A ?euron ( a perceptron)

The n3dimensiona! input vector 7is mapped into variab!e % b%means o* the sca!ar product and a non!inear *unction mapping

k

f

/eig#te

sum

n"ut

vector 7

out"ut y

2ctivation

unction

/eig#t

vector w

w&

w'

wn

x&

x'

xn

'sign%y

!/ampleFor

n

5i

kiixw +=

=

A Multi-#aer eed-orward ?eural


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Techniques 61

?etwor;

utput laer

%nput laer

Iidden laer

utput vector

%nput vector: X

wij

+=i

jiijj #wI

jIj e#

+= 1

1

''%1% jjjjj #(##)rr =

jkk

kjjj w)rr##)rr = '1%

ijijij #)rrlww '%+=

jjj )rrl'%+=

Iow A Multi-#aer ?eural ?etwor;


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Techniques 62

,or;s

The inputsto the netor correspond to the attributes measured *or

each training tup!e

$nputs are *ed simu!taneous!% into the units maing up the input

laer

The% are then eighted and *ed simu!taneous!% to a "idden laer

The number o* hidden !a%ers is arbitrar%, a!though usua!!% on!% one The eighted outputs o* the !ast hidden !a%er are input to units

maing up the output laer, hich emits the netorfs prediction

The netor is eed-orwardin that none o* the eights c%c!es bac

to an input unit or to an output unit o* a previous !a%er

rom a statistica! point o* vie, netors per*orm nonlinear

re!ression:


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Techniques 6

&efnin! a ?etwor; Topolo!

irst decide the networ; topolo!: R o* units in theinput la"er, R o* hidden la"ers)i* O 1+, R o* units in

each hidden la"er, and R o* units in the output la"er

Norma!i(ing the input va!ues *or each attribute

measured in the training tup!es to 040U140 ne inputunit per domain va!ue, each initia!i(ed to 0

utput, i* *or c!assi"cation and more than to

c!asses, one output unit per c!ass is used

nce a netor has been trained and its accurac% isunaccepta/le, repeat the training process ith a

di-erent net(or$ topolog"or a di-erent set of initial

(eights

9 ; i


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Techniques 67

9ac;propa!ation

$terative!% process a set o* training tup!es X compare the

netorfs prediction ith the actua! non target va!ue

or each training tup!e, the eights are modi"ed to +ini+i


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Techniques 65

p p !%nterpreta/ilit

/Bcienc% o* bacpropagation: /ach epoch )one interation

through the training set+ taes )FDF _ (+, ith FDF tup!es and

(eights, but R o* epochs can be e=ponentia! to n, the

number o* inputs, in the orst case

Au!e e=traction *rom netors: netor pruning

imp!i*% the netor structure b% removing eighted !insthat have the !east eVect on the trained netor

Then per*orm !in, unit, or activation va!ue c!ustering

The set o* input and activation va!ues are studied to derive

ru!es describing the re!ationship beteen the input andhidden unit !a%ers

ensitivit% ana!%sis: assess the impact that a given input

variab!e has on a netor output4 The no!edge gained

*rom this ana!%sis can be represented in ru!es

C"apter $. Classifcation and


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Techniques 66


hat is c!assi"cation# hat

is prediction#

$ssues regarding



tree induction




propagation


)?M+



%our neighbors+


-rediction


Mode! se!ection

ummar%

S5M Support 5ector Mac"ines


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Techniques 6

S5MSupport 5ector Mac"ines

. ne c!assi"cation method *or both !inear and

non!inear data

$t uses a non!inear mapping to trans*orm the origina!

training data into a higher dimension

ith the ne dimension, it searches *or the !inear

optima! separating h%perp!ane )i4e4, Idecision

boundar%J+

ith an appropriate non!inear mapping to a suBcient!%

high dimension, data *rom to c!asses can a!a%s be

separated b% a h%perp!ane

?M "nds this h%perp!ane using support vectors

)Iessentia!J training tup!es+ and margins )de"ned b%

the support vectors+

S5M Ii t d A li ti


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Techniques 6>

S5MIistor and Applications

?apni and co!!eagues )1@@2+Ugroundor *rom

?apni X Chervonenis statistica! !earning theor% in

1@60s

eatures: training can be s!o but accurac% is high

oing to their abi!it% to mode! comp!e= non!ineardecision boundaries )margin ma=imi(ation+

;sed both *or c!assi"cation and prediction

.pp!ications: handritten digit recognition, ob9ect recognition,

speaer identi"cation, benchmaring time3series

prediction tests

S5M 0 l P"il "


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Techniques 6@

S5M0eneral P"ilosop"

upport ?ectors

ma!! Margin 'arge Margin

ar! ns an uppor5 t


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Techniques 0

! pp5ectors

en a a s near S /l


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Techniques 1

Separa/le

m

'et data D be )>1, %1+, [, )>FDF, %FDF+, here >iis the set o* training

tup!es associated ith the c!ass !abe!s % i

There are in"nite !ines )h%perp!anes+ separating the to c!asses bute ant to "nd the best one )the one that minimi(es c!assi"cationerror on unseen data+

?M searches *or the h%perp!ane ith the !argest margin, i4e4,

+a7i+u+ +ar!inal "perplane)MML+

S5M #i l S /l


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Techniques 2

S5M#inearl Separa/le

. separating h%perp!ane can be ritten as

, > b H 0

here ,HP1, 2, [, nQ is a eight vector and b a sca!ar

)bias+

or 23D it can be ritten as

0 1=1 2=2H 0

The h%perp!ane de"ning the sides o* the margin:

L1: 0 1=1 2=2j 1 *or %i H 1, and

L2

: 0

1

=1

2

=2

E 1 *or %i

H E1

.n% training tup!es that *a!! on h%perp!anes L1or L2)i4e4, the

sides de"ning the margin+ are support vectors

This becomes a constrained (conve7) uadratic

opti+i


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Techniques

&ata

The comp!e=it% o* trained c!assi"er is characteri(ed b% the R

o* support vectors rather than the dimensiona!it% o* the data

The support vectors are the essentia! or critica! training

e=amp!es Uthe% !ie c!osest to the decision boundar% )MML+

$* a!! other training e=amp!es are removed and the training isrepeated, the same separating h%perp!ane ou!d be *ound

The number o* support vectors *ound can be used to compute

an )upper+ bound on the e=pected error rate o* the ?M

c!assi"er, hich is independent o* the data dimensiona!it%

Thus, an ?M ith a sma!! number o* support vectors can

have good genera!i(ation, even hen the dimensiona!it% o*

the data is high

S5M #inearl %nsepara/le

A *


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Techniques 7

S5M#inearl %nsepara/le

Trans*orm the origina! input data into a higherdimensiona! space

earch *or a !inear separating h%perp!ane in the

ne space

A '

S5M Oernel unctions


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Techniques 5

S5MOernel unctions

$nstead o* computing the dot product on the trans*ormed

data tup!es, it is mathematica!!% equiva!ent to insteadapp!%ing a erne! *unction Y)>i, >L+ to the origina! data, i4e4,

Y)>i, >L+ H k)>i+ k)>L+

T%pica! Yerne! unctions

?M can a!so be used *or c!assi*%ing mu!tip!e )O 2+ c!asses

and *or regression ana!%sis )ith additiona! user parameters+

Scalin! S5M / Iierarc"ical Micro-Clusterin!


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Techniques 6

Clusterin!

?M is not sca!ab!e to the number o* data ob9ects in terms o*

training time and memor% usage

IC!assi*%ing 'arge Datasets ;sing ?Ms ith Lierarchica!

C!usters -rob!emJ b% Lan9o u, Wiong ang, Wiaei Lan, YDD0

C&3?M )C!ustering3&ased ?M+


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Techniques

C9-S5M: Clusterin!-9ased S5M

Training data sets ma% not even "t in memor%

Aead the data set once )minimi(ing dis access+

Construct a statistica! summar% o* the data )i4e4,

hierarchica! c!usters+ given a !imited amount o* memor%

The statistica! summar% ma=imi(es the bene"t o* !earning?M

The summar% p!a%s a ro!e in inde=ing ?Ms

/ssence o* Micro3c!ustering )Lierarchica! inde=ing structure+

;se micro3c!uster hierarchica! inde=ing structure

provide "ner samp!es c!oser to the boundar% and

coarser samp!es *arther *rom the boundar%

e!ective de3c!ustering to ensure high accurac%

C Tree: Iierarc"ical Micro cluster


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Techniques >

C-Tree: Iierarc"ical Micro-cluster

C9 S5M Al!orit"+: utline


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Techniques @

C9-S5M Al!orit"+: utline

Construct to C3trees *rom positive and negativedata sets independent!% Need one scan o* the data set

Train an ?M *rom the centroids o* the root entries

De3c!uster the entries near the boundar% into thene=t !eve! The chi!dren entries de3c!ustered *rom the

parent entries are accumu!ated into the training

set ith the non3dec!ustered parent entries Train an ?M again *rom the centroids o* the

entries in the training set Aepeat unti! nothing is accumu!ated

Selective &eclusterin!


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Techniques >0

Selective &eclusterin!

C tree is a suitab!e base structure *or se!ective dec!ustering

De3c!uster on!% the c!uster /isuch that

DiE AiK Ds, here Diis the distance *rom the boundar% to

the center point o* /iand Aiis the radius o* /i Dec!uster on!% the c!uster hose subc!usters have

possibi!ities to be the support c!uster o* the boundar% Iupport c!usterJ: The c!uster hose centroid is a

support vector

'7peri+ent on Snt"etic &ataset


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November 16, 2015 Data Mining: Concepts andTechniques >1

'7peri+ent on Snt"etic &ataset

'7peri+ent on a #ar!e &ata Set


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'7peri+ent on a #ar!e &ata Set

S5M vs ?eural ?etwor;


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November 16, 2015 Data Mining: Concepts andTechniques >

S5M vs. ?eural ?etwor;

?M Ae!ative!% ne concept

Deterministic a!gorithm

Nice


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S5M 6elated #in;s

?M ebsite http:884erne!3machines4org8

Aepresentative imp!ementations

'$&?M: an eBcient imp!ementation o* ?M, mu!ti3c!ass

c!assi"cations, nu3?M, one3c!ass ?M, inc!uding a!so various

inter*aces ith 9ava, p%thon, etc4

?M3!ight: simp!er but per*ormance is not better than '$&?M,

support on!% binar% c!assi"cation and on!% C !anguage

?M3torch: another recent imp!ementation a!so ritten in C4

#iterature
http://www.kernel-machines.org/http://www.kernel-machines.org/


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#iterature

Itatistica! 'earning Theor%J b% ?apni: e=treme!% hard to

understand, containing man% errors too4

C4 W4 C4 &urges4

. Tutoria! on upport ?ector Machines *or -attern Aecognition 4

no(ledge Discoer" and Data ining, 2)2+, 1@@>4

&etter than the ?apnis boo, but sti!! ritten too hard *or

introduction, and the e=amp!es are so not3intuitive

The boo I.n $ntroduction to upport ?ector MachinesJ b% N4

Cristianini and W4 hae3Ta%!or

.!so ritten hard *or introduction, but the e=p!anation about

the mercers theorem is better than above !iteratures

The neura! netor boo b% La%ins

Contains one nice chapter o* ?M introduction

http://www.kernel-machines.org/papers/Burges98.ps.gzhttp://www.kernel-machines.org/papers/Burges98.ps.gz


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Prediction


is prediction#

$ssues regarding



tree induction




propagation


)?M+



%our neighbors+


-rediction


/nsemb!e methods

Mode! se!ection

ummar%

Associative Classifcation


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November 16, 2015 Data Mining: Concepts andTechniques >

Associative Classifcation


.ssociation ru!es are generated and ana!%(ed *or use in c!assi"cation

earch *or strong associations beteen *requent patterns

)con9unctions o* attribute3va!ue pairs+ and c!ass !abe!s

C!assi"cation: &ased on eva!uating a set o* ru!es in the *orm o*

-1 p2[ p!I.c!assH CJ )con*, sup+

h% eVective#

$t e=p!ores high!% con"dent associations among mu!tip!e attributes

and ma% overcome some constraints introduced b% decision3tree

induction, hich considers on!% one attribute at a time $n man% studies, associative c!assi"cation has been *ound to be more

accurate than some traditiona! c!assi"cation methods, such as C745

Tp ca Assoc at ve ass cat onMet"ods


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November 16, 2015 Data Mining: Concepts andTechniques >>

Met"ods

C&. )C!assi"cation &% .ssociation: 'iu, Lsu X Ma, YDD@>+

Mine association possib!e ru!es in the *orm o*

Cond3set )a set o* attribute3va!ue pairs+ c!ass !abe!

&ui!d c!assi"er: rgani(e ru!es according to decreasing

precedence based on con"dence and then support

CM.A )C!assi"cation based on Mu!tip!e .ssociation Au!es: 'i, Lan, -ei, $CDM01+ C!assi"cation: tatistica! ana!%sis on mu!tip!e ru!es

C-.A )C!assi"cation based on -redictive .ssociation Au!es: in X Lan, DM0+


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November 16, 2015 Data Mining: Concepts andTechniques >@

A Closer #oo; at CMA6

CM.A )C!assi"cation based on Mu!tip!e .ssociation Au!es: 'i, Lan, -ei, $CDM01+ /Bcienc%: ;ses an enhanced -3tree that maintains the distribution

o* c!ass !abe!s among tup!es satis*%ing each *requent itemset Au!e pruning henever a ru!e is inserted into the tree


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November 16, 2015 Data Mining: Concepts andTechniques @0

S%0M&=4)



91/124


Prediction


is prediction#

$ssues regarding



tree induction




propagation


)?M+



%our neighbors+


-rediction


/nsemb!e methods

Mode! se!ection

ummar%

#a


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#a


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November 16, 2015 Data Mining: Concepts andTechniques @

Met"ods

$nstance3based !earning: tore training e=amp!es and de!a% theprocessing )I!a(% eva!uationJ+ unti! a neinstance must be c!assi"ed

T%pica! approaches $3nearest neighbor approach

$nstances represented as points in a/uc!idean space4

'oca!!% eighted regression

Constructs !oca! appro=imation Case3based reasoning

;ses s%mbo!ic representations andno!edge3based in*erence

T"e k-?earest ?ei!"/or


94/124


Al!orit"+

.!! instances correspond to points in the n3Dspace The nearest neighbor are de"ned in terms o*

/uc!idean distance, dist)>1, >2+ Target *unction cou!d be discrete3 or rea!3

va!ued or discrete3va!ued, $3NN returns the most

common va!ue among the $training e=amp!esnearest toxq

?onoroi diagram: the decision sur*ace inducedb% 13NN *or a t%pica! set o* training e=amp!es

4

9

9 xq

9 9

9

9

.

.. .

&iscussion on t"e k-??Al it"


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Al!orit"+

3NN *or rea!3va!ued prediction *or a given unnontup!e

Aeturns the mean va!ues o* the$nearest neighbors

Distance3eighted nearest neighbor a!gorithm

eight the contribution o* each o* the neighborsaccording to their distance to the quer%xq


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Case 9ased 6easonin! (C96)

C&A: ;ses a database o* prob!em so!utions to so!ve ne prob!ems

tore s%mbo!ic description )tup!es or cases+Unot points in a /uc!ideanspace

.pp!ications: Customer3service )product3re!ated diagnosis+, !ega!

ru!ing

Methodo!og%

$nstances represented b% rich s%mbo!ic descriptions )e4g4, *unction

graphs+

earch *or simi!ar cases, mu!tip!e retrieved cases ma% be combined

Tight coup!ing beteen case retrieva!, no!edge3based reasoning,

and prob!em so!ving

Cha!!enges

ind a good simi!arit% metric

$nde=ing based on s%ntactic simi!arit% measure, and hen *ai!ure,

bactracing, and adapting to additiona! cases

C"apter $. Classifcation andP di ti


97/124

November 16, 2015 Data Mining: Concepts andTechniques @

Prediction


is prediction#

$ssues regarding



tree induction




propagation


)?M+



%our neighbors+ ther c!assi"cation methods

-rediction


/nsemb!e methods

Mode! se!ection

ummar%

0enetic Al!orit"+s (0A)


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November 16, 2015 Data Mining: Concepts andTechniques @>

0e e c !o s (0 )


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November 16, 2015 Data Mining: Concepts andTechniques @@

! pp

Aough sets are used to appro7i+atel or *rou!"lF

defne euivalent classes

. rough set *or a given c!ass C is appro=imated b% to sets:

a !oer appro=imation)certain to be in C+ and an upper

appro=imation)cannot be described as not be!onging to C+

inding the minima! subsets )reducts+ o* attributes *or

*eature reduction is N-3hard but a discerni/ilit +atri7

)hich stores the diVerences beteen attribute va!ues *or

each pair o* data tup!es+ is used to reduce the computation

intensit%

u


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November 16, 2015 Data Mining: Concepts andTechniques 100

Approac"es

u((% !ogic uses truth va!ues beteen 040 and 140 torepresent the degree o* membership )such as using*u((% membership graph+

.ttribute va!ues are converted to *u((% va!ues e4g4, income is mapped into the discrete categories

P!o, medium, highQ ith *u((% va!ues ca!cu!ated or a given ne samp!e, more than one *u((% va!ue

ma% app!% /ach app!icab!e ru!e contributes a vote *or

membership in the categories T%pica!!%, the truth va!ues *or each predicted categor%

are summed, and these sums are combined

C"apter $. Classifcation andP di ti


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Prediction


is prediction#

$ssues regarding



tree induction




propagation


)?M+




-rediction


/nsemb!e methods

Mode! se!ection

ummar%

,"at %s Prediction


102/124


)Numerica!+ prediction is simi!ar to c!assi"cation construct a mode! use mode! to predict continuous or ordered va!ue *or a given

input -rediction is diVerent *rom c!assi"cation

C!assi"cation re*ers to predict categorica! c!ass !abe! -rediction mode!s continuous3va!ued *unctions Ma9or method *or prediction: regression

mode! the re!ationship beteen one or more independentorpredictorvariab!es and a dependentor responsevariab!e

Aegression ana!%sis 'inear and mu!tip!e regression Non3!inear regression ther regression methods: genera!i(ed !inear mode!, -oisson

regression, !og3!inear mode!s, regression trees

#inear 6e!ression


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ea e! ess o

'inear regression: invo!ves a response variab!e % and a sing!e

predictor variab!e =

% H 0 1=

here 0)%3intercept+ and 1)s!ope+ are regression coeBcients

Method o* !east squares: estimates the best3"tting straight !ine

Mu!tip!e !inear regression: invo!ves more than one predictor variab!e

Training data is o* the *orm )>1, %1+, )>2, %2+,[, )>D&D, %FDF+

/=4 or 23D data, e ma% have: % H 0 1=1 2=2 o!vab!e b% e=tension o* !east square method or using ., 3

-!us

Man% non!inear *unctions can be trans*ormed into the above

=

=

=

22

1

0

22

1

'%

''%%

1 D

i

i

D

i

ii

xx

,,xx

w xw,w 15 =

?onlinear 6e!ression


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ome non!inear mode!s can be mode!ed b% a

po!%nomia! *unction . po!%nomia! regression mode! can be trans*ormed into

!inear regression mode!4 or e=amp!e,

% H 0 1= 2=2 =

convertib!e to !inear ith ne variab!es: =2 H =2, =H =

% H 0 1= 2=2 = ther *unctions, such as poer *unction, can a!so be

trans*ormed to !inear mode! ome mode!s are intractab!e non!inear )e4g4, sum o*

e=ponentia! terms+ possib!e to obtain !east square estimates through

e=tensive ca!cu!ation on more comp!e= *ormu!ae

!

t"er 6e!ression-9ased Models


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Trees Aegression tree: proposed in C.AT s%stem )&reiman et a!4 1@>7+

C.AT: C!assi"cation .nd Aegression Trees

/ach !ea* stores a continuous3alued prediction

$t is the aerage alue of the predicted attribute*or the

training tup!es that reach the !ea*

Mode! tree: proposed b% uin!an )1@@2+

/ach !ea* ho!ds a regression mode!Ua mu!tivariate !inear

equation *or the predicted attribute

. more genera! case than regression tree Aegression and mode! trees tend to be more accurate than

!inear regression hen the data are not represented e!! b% a

simp!e !inear mode!

Predictive Modelin! in Multidi+ensional&ata/ases


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Techniques 10

-redictive mode!ing: -redict data va!ues or construct

genera!i(ed !inear mode!s based on the database data ne can on!% predict va!ue ranges or categor% distributions Method out!ine:

Minima! genera!i(ation

.ttribute re!evance ana!%sis


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Techniques 10>

Prediction: Cate!orical &ata


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Techniques 10@



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Techniques 110

Prediction


is prediction#

$ssues regarding



tree induction




propagation


)?M+




-rediction


/nsemb!e methods

Mode! se!ection

ummar%

Classifer AccuracMeasures

C1 C2

C1 True positive a!senegative


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Techniques 111

.ccurac% o* a c!assi"er M, acc)M+: percentage o* test set tup!es that arecorrect!% c!assi"ed b% the mode! M

/rror rate )misc!assi"cation rate+ o* M H 1 E acc)M+ > 000 >642

tota! 66 267 1000

0

@5452

g

C2 a!sepositive

True negative

Predictor 'rror Measures


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Techniques 112

Measure predictor accurac%: measure ho *ar oV the predicted

va!ue is *rom the actua! non va!ue #oss unction: measures the error bet4 %iand the predicted va!ue

%i

.bso!ute error: F %iE %iF

quared error: )%iE %i+2

Test error )genera!i(ation error+: the average !oss over the test set

Mean abso!ute error: Mean squared error:

Ae!ative abso!ute error: Ae!ative squared error:

The mean squared3error e=aggerates the presence o* out!iers

-opu!ar!% use )square+ root mean3square error, simi!ar!%, root

re!ative squared error

d

,,d

i

ii=

1

2


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Techniques 11

Classifer or Predictor (%) Lo!dout method


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Techniques 117

Classifer or Predictor (%%)

&ootstrap

ors e!! ith sma!! data sets

amp!es the given training tup!es uni*orm!% (ith replacement

i4e4, each time a tup!e is se!ected, it is equa!!% !ie!% to be

se!ected again and re3added to the training set

evera! boostrap methods, and a common one is .$2 /oostrap uppose e are given a data set o* d tup!es4 The data set is samp!ed

d times, ith rep!acement, resu!ting in a training set o* d samp!es4

The data tup!es that did not mae it into the training set end up

*orming the test set4 .bout 642 o* the origina! data i!! end up in

the bootstrap, and the remaining 64> i!! *orm the test set )since )1E 18d+d e31H 046>+

Aepeat the samp!ing procedue times, overa!! accurac% o* the

mode!:''%6845'%6045%'% 9

1

9 settraini

k

i

settesti -acc-acc-acc +==



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Techniques 115

Prediction


is prediction#

$ssues regarding



tree induction




propagation


)?M+




-rediction


/nsemb!e methods

Mode! se!ection

ummar%

'nse+ e Met o s: %ncreas n! t eAccurac


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Techniques 116

/nsemb!e methods ;se a combination o* mode!s to increase accurac% Combine a series o* !earned mode!s, M1, M2, [, M,

ith the aim o* creating an improved mode! M_ -opu!ar ensemb!e methods

&agging: averaging the prediction over a co!!ection o*

c!assi"ers &oosting: eighted vote ith a co!!ection o*

c!assi"ers /nsemb!e: combining a set o* heterogeneous

c!assi"ers

a!! n!: oos rapA!!re!ation


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Techniques 11

.na!og%: Diagnosis based on mu!tip!e doctors ma9orit% vote

Training

/ach c!assi"er Mireturns its c!ass prediction The bagged c!assi"er M_ counts the votes and assigns the c!ass

ith the most votes to > -rediction: can be app!ied to the prediction o* continuous va!ues b%

taing the average va!ue o* each prediction *or a given test tup!e

.ccurac% *ten signi"cant better than a sing!e c!assi"er derived *rom D or noise data: not considerab!% orse, more robust -roved improved accurac% in prediction

9oostin!


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Techniques 11>

.na!og%: Consu!t severa! doctors, based on a combination o* eighted

diagnosesUeight assigned based on the previous diagnosis accurac% Lo boosting ors#

eights are assigned to each training tup!e

. series o* c!assi"ers is iterative!% !earned

.*ter a c!assi"er Miis !earned, the eights are updated to a!!o the

subsequent c!assi"er, Mi1, to pa% more attention to the training

tup!es that ere misc!assi"ed b% Mi The "na! M_ combines the votes o* each individua! c!assi"er, here

the eight o* each c!assi"erfs vote is a *unction o* its accurac%

The boosting a!gorithm can be e=tended *or the prediction o*

continuous va!ues

Comparing ith bagging: boosting tends to achieve greater accurac%,

but it a!so riss over"tting the mode! to misc!assi"ed data

a oos reun an c ap re81QQR)


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Techniques 11@

1, %1+, [, )>d, %d+ $nitia!!%, a!! the eights o* tup!es are set the same )18d+ L4

C!assi"er Mierror rate is the sum o* the eights o* themisc!assi"ed tup!es:

The eight o* c!assi"er Mis vote is '%

'%1log

i

i

-error

-error

=d

j

ji errw-error '%'% X



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Techniques 120

Prediction


is prediction#

$ssues regarding



tree induction




propagation


)?M+




-rediction


/nsemb!e methods

Mode! se!ection

ummar%

Model Selection: 6CCurves


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Techniques 121

AC )Aeceiver perating

Characteristics+ curves: *or visua!comparison o* c!assi"cation mode!s

riginated *rom signa! detection theor%

hos the trade3oV beteen the true

positive rate and the *a!se positive rate

The area under the AC curve is a

measure o* the accurac% o* the mode!

Aan the test tup!es in decreasing order:

the one that is most !ie!% to be!ong to

the positive c!ass appears at the top o*the !ist

The c!oser to the diagona! !ine )i4e4, the

c!oser the area is to 045+, the !ess

accurate is the mode!

?ertica! a=isrepresents the truepositive rate

Lori(onta! a=is rep4

the *a!se positiverate The p!ot a!so shos

a diagona! !ine . mode! ith per*ect

accurac% i!! have



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Techniques 122

Prediction


is prediction#

$ssues regarding



tree induction




propagation


)?M+




-rediction


/nsemb!e methods

Mode! se!ection

ummar%

Su++ar (%)


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Techniques 12

C!assi"cation andpredictionare to *orms o* data ana!%sis that

can be used to e=tract mode!sdescribing important data c!assesor to predict *uture data trends4

/Vective and sca!ab!e methods have been deve!oped *or decision

trees induction, Naive &a%esian c!assi"cation, &a%esian be!ie*

netor, ru!e3based c!assi"er, &acpropagation, upport ?ector

Machine )?M+, associative c!assi"cation, nearest neighbor

c!assi"ers,and case3based reasoning, and other c!assi"cation

methods such as genetic a!gorithms, rough set and *u((% set

approaches4

'inear, non!inear, and genera!i(ed !inear mode!s o* regressioncanbe used *or prediction4 Man% non!inear prob!ems can be converted

to !inear prob!ems b% per*orming trans*ormations on the predictor

variab!es4 Aegression treesand mode! treesare a!so used *or

prediction4

Su++ar (%%)


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trati"ed 3*o!d cross3va!idationis a recommended method *or

accurac% estimation4 &agging and boostingcan be used to

increase overa!! accurac% b% !earning and combining a series o*

individua! mode!s4

igni"cance testsand AC curvesare use*u! *or mode! se!ection

There have been numerous comparisons o* the diVerent

c!assi"cation and prediction methods, and the matter remains a

research topic

No sing!e method has been *ound to be superior over a!! others *or

a!! data sets $ssues such as accurac%, training time, robustness, interpretabi!it%,

Data Mining Classification and Prediction by Dr. Tanvir Ahmed

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Transcript of Data Mining Classification and Prediction by Dr. Tanvir Ahmed