INTERACTIVE

CLASSIFICATION WITH

INCLAS:

GET MORE FROM LESS

M.sc.ing. ILZE BIRZNIECE

Predictiveanalytics

Datamining

Machinelearning

Businessintelligence

Artificialintelligence

Classifi-cation

2

DATA

CLASSIFICATION

Learning from the past experience E.g. weather prediction, medical diagnostic, organizing

documents, credit scoring etc.

Formalized data (attributes, classes)

Experience (training examples)

Various methods and tools for classification task 3

No. Income

(Eur/month)

Has loan Client

(month)

... Outcome

1 1200 yes 23 .. Untrustful

2 900 no 50 .. Trustful

..

x 1700 yes 2 .. ?

WHY NEW APPROACH?

4Incompleteness of classifier

Small training base

Experts do not trust fully

automated solutions

Poor

performance

Complex and hard to formalize domain

Inappropriateness of automatic classification

methods for every domain where machine

learning techniques could be applied to

Practical need to help experts in area of curricula

comparison

INTERACTIVE CLASSIFICATION

5

Automaticclassification

Manualclassification

Interactive(semi-

automatic) classification

INTERACTIVE APPROACH

Interactive approach includes:

Featuring classifier with ability to detect unclassified

and uncertainly classified objects

6

•Updating

classifier

•Using

transparent

classifier

•Asking for

the help of

human

FEATURES OF INCLAS (INTERACTIVE

CLASSIFICATION SYSTEM)

Dealing with

multi-label class membership

semi-structured and unstructured data

small initial training base

many classes with similar probability to appear

various confidence thresholds

Involving expert in order to achieve better results

7

o single-label

o structured

o sufficient

o two classes

o traditional

INCLAS MODEL

8

EXPERIMENTAL RESULTS

Number of misclassified objects can be (significantly)

reduced if an interactive classification system is

applied

9

0,267

0,366

0,367

0,85

0,060,09

(Partly)

Correct

Misclassified

UnClassified

Study course comparison Medical diagnostics*

* From Computational Medicine Center's 2007

Medical Natural Language Processing Challenge

VISION

10

InClaS:

get more from lessknowledge data

Broadening application areas of InClaS

Extending current prototype

TARGETED ADVERTISING

11

Lowering advertising costs

Adressing right audience

Setting confidence threshold

Consulting with expert

DOCUMENT (ARTICLE, PICTURE ETC.)

ORGANIZATION

12

Multiple categories for each object

Limited amount of categorized data

Multi-label classification

Overall approach for using and

improving weak classifiers

RECOMMENDATIONS OF INCLAS

APPLICATION

The use of the interactive classification system is

feasible in areas where:

Human-expert is available

Problem domain is defined by the attributes which

are comprehensible for the expert

The interactive classification approach is more

appropriate in areas where at least one of the

following statements holds:

It is essential to receive a correct classification for as

much objects as possible, and it is acceptable to

invest the expert’s work and time to achieve it

It is hard to extract or define domain features

Only a small initial learning set is available

13

LOOKING FOR COOPERATION

Ilze Birzniece

ilze.birzniece@rtu.lv

Summary of Doctoral Thesis

Development of Interactive Inductive

Learning Based Classification System's

Model 14