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LCMine: An efficient algorithm for mining discriminative regularities and its application in supervised classification
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DOI：10.1016/j.patcog.2010.04.008.png)
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Abstract
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In this paper, we introduce an efficient algorithm for mining discriminative regularities on databases with mixed and incomplete data. Unlike previous methods, our algorithm does not apply an a priori discretization on numerical features; it extracts regularities from a set of diverse decision trees, induced with a special procedure. Experimental results show that a classifier based on the regularities obtained by our algorithm attains higher classification accuracy, using fewer discriminative regularities than those obtained by previous pattern-based classifiers. Additionally, we show that our classifier is competitive with traditional and state-of-the-art classifiers. (C) 2010 Elsevier Ltd. All rights reserved.
Keywords:
Discriminative regularities
Emerging patterns
Mixed incomplete data
Comprehensible classifiers
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