Publication
Title
Active learning-based pedagogical rule extraction
Author
Abstract
Many of the state-of-the-art data mining techniques introduce nonlinearities in their models to cope with complex data relationships effectively. Although such techniques are consistently included among the top classification techniques in terms of predictive power, their lack of transparency renders them useless in any domain where comprehensibility is of importance. Rule-extraction algorithms remedy this by distilling comprehensible rule sets from complex models that explain how the classifications are made. This paper considers a new rule extraction technique, based on active learning. The technique generates artificial data points around training data with low confidence in the output score, after which these are labeled by the black-box model. The main novelty of the proposed method is that it uses a pedagogical approach without making any architectural assumptions of the underlying model. It can therefore be applied to any black-box technique. Furthermore, it can generate any rule format, depending on the chosen underlying rule induction technique. In a large-scale empirical study, we demonstrate the validity of our technique to extract trees and rules from artificial neural networks, support vector machines, and random forests, on 25 data sets of varying size and dimensionality. Our results show that not only do the generated rules explain the black-box models well (thereby facilitating the acceptance of such models), the proposed algorithm also performs significantly better than traditional rule induction techniques in terms of accuracy as well as fidelity.
Language
English
Source (journal)
IEEE transactions on neural networks and learning systems
Publication
2015
ISSN
2162-237X
2162-2388
DOI
10.1109/TNNLS.2015.2389037
Volume/pages
26 :11 (2015) , p. 2664-2677
ISI
000363242800005
Full text (Publisher's DOI)
Full text (publisher's version - intranet only)
UAntwerpen
Faculty/Department
Research group
Publication type
Subject
Affiliation
Publications with a UAntwerp address
External links
Web of Science
Record
Identifier
Creation 30.01.2015
Last edited 09.10.2023
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