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Title
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Active learning-based rule extraction for regression
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Author
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Abstract
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| 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. |
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Language
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English
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Source (book)
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2012 IEEE 12th International Conference on Data Mining Workshops, Brussels (Belgium)
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Publication
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S.l.
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IEEE
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2012
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ISBN
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978-0-7695-4925-5
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Volume/pages
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(2012)
, p. 926-933
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ISI
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000320946500134
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Full text (Publisher's DOI)
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