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2012 IEEE 12th International Conference on Data Mining Workshops
Active Learning Based Rule Extraction for Regression
Brussels, Belgium Belgium
December 10-December 10
ISBN: 978-1-4673-5164-5
Advances in data mining have led to algorithms that produce accurate regression models for large and difficult to approximate data. 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 rule sets 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.
Index Terms:
Data mining,Support vector machines,Data models,Artificial neural networks,Accuracy,Predictive models,Prediction algorithms,datamining,rule extraction,regression,alpa
Citation:
Enric Junque de Fortuny, David Martens, "Active Learning Based Rule Extraction for Regression," icdmw, pp.926-933, 2012 IEEE 12th International Conference on Data Mining Workshops, 2012
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