A new emerging pattern mining algorithm and its application in supervised classification

Authors:
Milton García-Borroto;José Francisco Martínez-Trinidad;Jesús Ariel Carrasco-Ochoa
Affiliations:
Centro de Bioplantas, Cuba;Instituto Nacional de Astrofísica, Óptica y Electrónica, Puebla, México;Instituto Nacional de Astrofísica, Óptica y Electrónica, Puebla, México
Venue:
PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
Year:
2010

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Abstract

Obtaining an accurate class prediction of a query object is an important component of supervised classification. However, it could be important to understand the classification in terms of the application domain, mostly if the prediction disagrees with the expected results. Many accurate classifiers are unable to explain their classification results in terms understandable by an application expert. Emerging Pattern classifiers, on the other hand, are accurate and easy to understand. However, they have two characteristics that could 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, which uses an accurate estimation of the support threshold. Experimental results show that our classifier attains higher accuracy than other understandable classifiers, while being competitive with Nearest Neighbors and Support Vector Machines classifiers.