Lecture19

Introduction to MachineIntroduction to Machine LearningLearning

Lecture 19Lecture 19Genetic Fuzzy Systems

Albert Orriols i Puightt // lb t i l thttp://www.albertorriols.net

[email protected]

Artificial Intelligence – Machine Learningg gEnginyeria i Arquitectura La Salle

Universitat Ramon Llull

Recap of Lectures 5-18Supervised learningp g

Data classification

Labeled dataLabeled data

Build a model that covers all the space

Uns per ised learningUnsupervised learningClustering

Unlabeled data

Group similar objects

Association rule analysis

Unlabeled data

Slide 2

Get the most frequent/important associations

Artificial Intelligence Machine Learning

Today’s Agenda

Fuzzy LogicsFuzzy SystemsGenetic Fuzzy SystemsGenetic Fuzzy Systems

Slide 3Artificial Intelligence Machine Learning

Fuzzy LogicsLooking up in the dictionary…g p y

Fuzzy = “not clear, distinct, or precise; blurred”

Th ld i i i t l bl dThe world is imprecise, not clear, blurred… The world is fuzzy!

Definition of fuzzy logicsy gA form of knowledge representation suitable for notions that cannot be defined precisely, but which depend upon their p y, p pcontexts

Let’s go from true and false (traditional logics) to something more powerful

Slide 4

something more powerful


Fuzzy LogicsTraditional logic representationg p

Slow Fast

Logic repSlow speed = 0

Fast speed = 1Fast speed 1


Fuzzy LogicsHow fast is fast?

Definition of slow and fast depend on the eyes of the beholder

N t l l t i bj ti tNatural language contains many subjective terms

H I d l ith thi ?How can I deal with this?

Very slow Slow Fast Very fast

These four are linguistic terms

Still, I need to define the semantics of each

Slide 6

St , eed to de e t e se a t cs o eaclinguistic term!


Fuzzy LogicsClassical view

Define intervals: very slow [0 – 0.25]very slow [0 0.25]slow [0.25 – 0.5]fast [0.5 – 0.75]very fast [0.75 – 1]

Fuzzy logics viewy gConsider the degree with which each observation belongs to each linguistic term

Define a membership function


Fuzzy LogicsMember ship functions

Semantics of the system

Very slow Slow Fast Very fast

0 0.25 0.50 0.75 1


Fuzzy LogicsMany different membership functions. Some of them arey p

1

a b c d


Fuzzy SystemsFuzzy systemsy y

are fundamental methodologies to represent and process linguistic informationgu s c o a o

use fuzzy logic to either represent the knowledge or model the interactions and relationships among the system variables in e ac o s a d e a o s ps a o g e sys e a ab esenvironments where there is uncertainty and imprecision.

E.g. of knowledge representation:If john is tall and fast then strong

Genetic fuzzy systemsy yThe use of genetic/evolutionary algorithms (GAs) to design fuzzy systemsy y


GFS


GFSTwo key elements:y

Fuzzy systemIn our case we will focus on rule-based systemsIn our case, we will focus on rule-based systems

Genetic algorithm

Fuzzy systemy y


Fuzzy Rule-Based SystemsRule base

If size is small and weight is small then quality is bad

If i i ll d i ht i l th lit i diIf size is small and weight is large then quality is medium

If size is large and weight is small then quality is medium

If size is large and weight is large then quality is good

Data baseData base


Fuzzy Rule-Based SystemsOperation of the inference system

Centre ofgravity


Fuzzy Rule-Based SystemsGreat, I know how to infer… But who gives me, g

The rules

Th i f ti f th d t b (th ti )The information of the data base (the semantics)

The inference engineInference systemDefuzzification methods

Use a genetic algorithm for this task


Recall GAs?

Individual 1Individual 2

Fit. 1Fit. 2

Population

Individual i

Individual j

Individual 1

Individual 1Individual 2

Initialization

PopulationIndividual 2

...Individual n

Fit. 2...

Fit. n

Individual 1

Individual n...

Individual n

Individual i’

Individual j’

I di id l 1’

Individual i’’

Individual j’’Mutation

Individual 1’

Individual n’Individual 1’’

Individual n’’

Selection + Mutation: Continuous improvement and local search

Selection + Recombination: Innovation


Selection + Recombination: Innovation

Where Do we Use the GA?Taxonomy of GFS (Herrera, 2008)y ( , )


TopicsWe are going to seeg g1. Genetic tuning

G ti l l i2. Genetic rule learning

3. Genetic rule selection

4. Genetic DB learning

5. Simultaneous genetic learning of KB components5 S u ta eous ge et c ea g o co po e ts

6. Genetic learning of KB components and inference engine parameterspa a ete s

1st i thi l t 2nd 5th i t l t1st seen in this lecture. 2nd-5th seen in next lecture

Information based on the paper Herrera (2009) and the di t ti

Slide 19

corresponding presentation


1. Genetic TuningTypically membership functionsyp y p

are defined by domain experts

j t l t d f l f t i l t idare just selected from general forms: triangles, trapezoids, Gaussian…

B t ld h b tt b hi f ti ?But, could we have better membership functions?Let a GA tune the membership functions

Also, tune the inference parameters


1. Genetic TuningHow do we apply the GA?

So, we are modifyingthe partitions of the feature space


1. Genetic TuningAn example: Tuning triangular membership functionsp g g p

Each chromosome encodes a different DB definition2 vars x 3 ling. labels = 6 mem. functionsgTriangles 3 real values to code themChromosome length = 18 genes

Slide 22

Note that the RB remains unchanged!


Next Class

N t lNext class1. Genetic rule learning

2. Genetic rule selection

3. Genetic DB learning3. Genetic DB learning

4. Simultaneous genetic learning of KB components

G ti l i f KB t d i f i5. Genetic learning of KB components and inference engine parameters

Applications


Introduction to MachineIntroduction to Machine LearningLearning

Lecture 19Lecture 19Genetic Fuzzy Systems

Albert Orriols i Puightt // lb t i l thttp://www.albertorriols.net

[email protected]

Artificial Intelligence – Machine Learningg gEnginyeria i Arquitectura La Salle

Universitat Ramon Llull

Lecture19

Education

Transcript of Lecture19