LCMine: An efficient algorithm for mining discriminative regularities and its application in supervised classification |
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Authors: | Milton García-Borroto [Author Vitae] José Fco Martínez-Trinidad [Author Vitae] |
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Affiliation: | a Centro de Bioplantas, Carretera a Moron km 9, Ciego de Avila, Cuba b Instituto Nacional de Astrofísica, Óptica y Electrónica, Luis Enrique Erro No. 1, Sta. María Tonanzintla, Puebla, C.P. 72840 México, Mexico c Advanced Technologies Application Center, 7a #21812 e/ 218 y 222, Siboney, Playa, Habana, Cuba |
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Abstract: | 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. |
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Keywords: | Discriminative regularities Emerging patterns Mixed incomplete data Comprehensible classifiers |
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