Post on 15-Jan-2016
transcript
Active subgroup mining
for descriptive induction
tasks
Dragan Gamberger
Rudjer Bošković Instute, Zagreb
Zdenko Sonicki
University of Zagreb
Talk overview:
- descriptive induction- active subgroup mining - subgroup discovery- data mining server- a real medical example
Descriptive induction is aimed at generating (inducing) knowledge that is understandable (interpretable) by humans.
It is different from classification aimed induction where the main goal is high classification quality (but induced classification schemes are typically too complex for human interpretation).
Main properties of descriptive induction:
- simple rules
- reasonable prediction quality (both on available and future cases)
Main problem: overfitting
functional genomics domain has 150 examples with 16000 measured attribute values
- descriptive induction- active subgroup mining - subgroup discovery- data mining server- a real medical example
Active subgroup mining is a data analysis approach specially developed for medical applications (but applicable also for other domains).
It is based on the observation that expert knowledge (in medical domains it means knowledge
and experience of medical doctors) is very important for the quality of obtained results.
In active subgroup mining the expert is positioned in the center of the process and machine learning (subgroup discovery) is only a tool that helps him in the data analysis process.
definition of task(s)
induction of models
presentation
visualization
integration
statistical evaluatio
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selection of models
expert
subgroup discovery
- descriptive induction- active subgroup mining - subgroup discovery- data mining server- a real medical example
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classical versus subgroup discovery
induction
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very specific subgroup very sensitive subgroup
generality – the main parameter of the subgroup induction process
Subgroup discovery is a beam search algorithm which generates short rules in the form of conjunctions of conditions.
Conditions are based on the values of available attributes.
example:
CHD <- age > 53 AND T.CH > 6.1 AND BMI < 30
- descriptive induction- active subgroup mining - subgroup discovery- data mining server- a real medical example
dms.irb.hr
meningoencephalitis domain
subgroup describing bacteria in contrast to the virus type disease
- descriptive induction- active subgroup mining - subgroup discovery- data mining server- a real medical example
Conclusions:
-descriptive induction and active subgroup mining are novel concepts potentially very interesting for data analysis and knowledge induction in medical applications
- active and central role of medical experts is essential
- we have extensive and positive experience with these methodology on different medical domains but no experience in constructing medical guidelines. For such applications potentially useful might be:
- detection of decision points for numerical attributes
- detection of apparent but significant contradictions
- explicit noise detection