Studies in Informatics and Control – ICI Bucharest
Yi Peng
School of Management and Economics, University of Electronic Science and Technology of China
No.4, Sec 2, North Jianshe Rd, Chengdu, 610054, China
Gang Kou
School of Management and Economics, University of Electronic Science and Technology of China
No.4, Sec 2, North Jianshe Rd, Chengdu, 610054, China
Daji Ergu
Southwest University for Nationalities
Chengdu, 610200, China
Wenshuai Wu
School of Management and Economics, University of Electronic Science and Technology of China
No.4, Sec 2, North Jianshe Rd, Chengdu, 610054, China
Yong Shi
CAS Research Center on Fictitious Economy and Data Sciences
Beijing 100080, China
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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CITE THIS PAPER AS:
Yi PENG, Gang KOU, Daji ERGU, Wenshuai WU, Yong SHI, An Integrated Feature Selection and Classification Scheme, Studies in Informatics and Control, ISSN 1220-1766, vol. 21 (3), pp. 241-248, 2012. https://doi.org/10.24846/v21i3y201202