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General Information
Prof. Wael Badawy
Department of Computing and Information Systems Umm Al Qura University, Canada
I'm happy to take on the position of editor in chief of IJCTE. We encourage authors to submit papers concerning any branch of computer theory and engineering.
IJCTE 2011 Vol.3(5): 643-651 ISSN: 1793-8201
DOI: 10.7763/IJCTE.2011.V3.385

A Feature Selection Method Using Misclassified Patterns

D. M. Shawky and A. F. Ali
Abstract—Feature selection (FS) is a key step in the data mining process. In FS, the objective is to select the smallest subset of features that reduces complexity and ensures generalization. In this paper, we present a combined filter-wrapper feature selection approach using misclassified data. The learning process starts with only one feature, which gives a large number of misclassified patterns. Only these patterns are used to select the next best feature which is added to the first one. By focusing on the misclassified patterns, the learner is undistracted and hence, it can select the relevant features more effectively and faster. The process continues until the classification results are within the required accuracy. The approach is applied to three datasets with high dimensional features using a variety of selection models and search strategies. Experimental results demonstrate the efficiency of the proposed approach in the two-class classification tasks.

Index Terms—Feature selection, misclassified patterns, pattern classification.

D. M. Shawky is with the Engineering Mathematics Department, Cairo University, Egypt, (e-mail: doaashawky@yahoo.com).
A. F. Ali is with the Biomedical Engineering Department, Helwan University, Cairo, Egypt, (e-mail: ahmed.farag@mcit.gov.eg).


Cite: D. M. Shawky and A. F. Ali, "A Feature Selection Method Using Misclassified Patterns," International Journal of Computer Theory and Engineering vol. 3, no. 5, pp. 643-651, 2011.
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