Título

LCMine: An efficient algorithm for mining discriminative regularities and its application in supervised classification

Autor

MILTON GARCÍA BORROTO

JOSE FRANCISCO MARTINEZ TRINIDAD

JESUS ARIEL CARRASCO OCHOA

MIGUEL ANGEL MEDINA PEREZ

Nivel de Acceso

Acceso Abierto

Resumen o descripción

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.

Editor

Elsevier Ltd.

Fecha de publicación

2010

Tipo de publicación

Artículo

Versión de la publicación

Versión aceptada

Formato

application/pdf

Idioma

Inglés

Audiencia

Estudiantes

Investigadores

Público en general

Sugerencia de citación

García-Borroto, M., et al., (2010). LCMine: An efficient algorithm for mining discriminative regularities and its application in supervised classification, Pattern Recognition, (43): 3025–3034

Repositorio Orígen

Repositorio Institucional del INAOE

Descargas

414

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