Publication
Title
Active learning-based rule extraction for regression
Author
Abstract
Advances in data mining have led to algorithms that produce accurate regression models for large and difficult to approximate datasets. Many of these use non-linear models to handle complex data-relationships in the input data. Their lack of transparency, however, is problematic since comprehensibility is a key requirement in many potential application domains. Rule-extraction algorithms have been proposed to solve this problem for classification by extracting comprehensible rulesets from the often better performing, complex models. We present a new pedagogical rule extraction algorithm for regression, based on active learning, which can be combined with any existing rule induction technique. Empirical results show that the proposed ALPA-R rule extraction method improves on classical rule induction techniques, both in accuracy and fidelity.
Language
English
Source (book)
2012 IEEE 12th International Conference on Data Mining Workshops, Brussels (Belgium)
Publication
S.l. : IEEE, 2012
ISBN
978-0-7695-4925-5
Volume/pages
(2012), p. 926-933
ISI
000320946500134
Full text (Publisher's DOI)
UAntwerpen
Faculty/Department
Research group
Publication type
Subject
Affiliation
Publications with a UAntwerp address
External links
Web of Science
Record
Identification
Creation 19.12.2012
Last edited 30.07.2017
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