Cascading an emerging pattern based classifier

Authors:
Milton García-Borroto;José Fco. Martínez-Trinidad;Jesús Ariel Carrasco-Ochoa
Affiliations:
Centro de Bioplantas, Ciego de Avila, Cuba and Instituto Nacional de Astrofísica, Óptica y Electrónica, Puebla, México, C.P.;Instituto Nacional de Astrofísica, Óptica y Electrónica, Puebla, México, C.P.;Instituto Nacional de Astrofísica, Óptica y Electrónica, Puebla, México, C.P.
Venue:
MCPR'10 Proceedings of the 2nd Mexican conference on Pattern recognition: Advances in pattern recognition
Year:
2010

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Abstract

Emerging Pattern classifiers are accurate and easy to understand classifiers. However, they have two characteristics that can degrade their accuracy: global discretization of numerical attributes and high sensitivity to the support threshold value. In this paper, we introduce a novel algorithm to find emerging patterns without global discretization. Additionally, we propose a new method for building cascades of emerging pattern classifiers, which combines the higher accuracy of classifying with higher support thresholds with the lower levels of abstention of classifying with lower thresholds. Experimental results show that our cascade attains higher accuracy than other state-of-the-art classifiers, including one of the most accurate emerging pattern based classifier.