An Integrated Feature Selection and Classification Scheme

Yi Peng, Gang Kou, Daji Ergu, Wenshuai Wu, Yong Shi

Abstract

Irrelevant and redundant features may not only deteriorate the performances of classifiers, but also slow the prediction process. Another problem in prediction is the availability of a large number of classification models. How to choose a satisfactory classifier is an important yet understudied task. The goal of this paper is to propose an integrated scheme for feature selection and classifier evaluation in the context of prediction. It combines traditional feature selection techniques and multi-criteria decision making (MCDM) methods in an attempt to increase the accuracies of classification models and identify appropriate classifiers for different types of data sets.

Keywords

multi-criteria decision making (MCDM); feature selection; classification.

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