1. INTERACTIVE
CLASSIFICATION WITH
INCLAS:
GET MORE FROM LESS
M.sc.ing. ILZE BIRZNIECE

2. Predictive
analytics
Data
mining
Classification
Artificial
intelligence
Business
intelligence
Machine
learning
DATA
2

3. CLASSIFICATION

Learning from the past experience
E.g. weather prediction, medical diagnostic, organizing
documents, credit scoring etc.
 Formalized data (attributes, classes)
 Experience (training examples)

No.
Income
Has loan
(Eur/month)
Client
(month)
...
Outcome
1
1200
yes
23
..
Untrustful
2
900
no
50
..
Trustful
1700
yes
2
..
?
..
x

Various methods and tools for classification task
3

4. WHY NEW APPROACH?
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

Experts do not trust fully
automated solutions
Poor
performance
Complex and hard to formalize domain
Small training base
Incompleteness of classifier
4

5. INTERACTIVE CLASSIFICATION
Automatic
classification
Manual
classification
Interactive
(semiautomatic)
classification
5

6. INTERACTIVE APPROACH
Interactive approach includes:
 Featuring classifier with ability to detect unclassified
and uncertainly classified objects
•Asking for
the help of
human
•Updating
classifier
•Using
transparent
classifier
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7. FEATURES OF INCLAS (INTERACTIVE
CLASSIFICATION SYSTEM)

Dealing with






o
multi-label class membership
o
semi-structured and unstructured data
o
small initial training base
many classes with similar probability to appear o
o
various confidence thresholds
single-label
structured
sufficient
two classes
traditional
Involving expert in order to achieve better results
7

8. INCLAS MODEL
8

9. EXPERIMENTAL RESULTS

Number of misclassified objects can be (significantly)
reduced if an interactive classification system is
applied
Medical diagnostics*
Study course comparison
0,06
0,367
0,267
0,366
(Partly)
Correct
Misclassified
0,09
0,85
UnClassified
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* From Computational Medicine Center's 2007
Medical Natural Language Processing Challenge

10. VISION
InClaS:
get more from less
knowledge
data
Broadening application areas of InClaS
 Extending current prototype

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11. TARGETED ADVERTISING


Lowering advertising costs
Adressing right audience
Setting confidence threshold
 Consulting with expert

11

12. DOCUMENT (ARTICLE, PICTURE ETC.)
ORGANIZATION


Multiple categories for each object
Limited amount of categorized data
Multi-label classification
 Overall approach for using and
improving weak classifiers

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13. 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

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14. LOOKING FOR COOPERATION
Ilze Birzniece
ilze.birzniece@rtu.lv
Summary of Doctoral Thesis
Development of Interactive Inductive
Learning Based Classification System's
Model
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