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
Materias
Discriminative regularities - (DISCRIMINATIVE REGULARITIES) Emerging patterns - (EMERGING PATTERNS) Mixed incomplete data - (MIXED INCOMPLETE DATA) Comprehensible classifiers - (COMPREHENSIBLE CLASSIFIERS) CIENCIAS FÍSICO MATEMÁTICAS Y CIENCIAS DE LA TIERRA - (CTI) MATEMÁTICAS - (CTI) CIENCIA DE LOS ORDENADORES - (CTI) CIENCIA DE LOS ORDENADORES - (CTI)
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
Recurso de información
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
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