期刊
INFORMATION PROCESSING LETTERS
卷 116, 期 2, 页码 203-215出版社
ELSEVIER
DOI: 10.1016/j.ipl.2015.07.005
关键词
Design of algorithms; Feature selection; Correlation measure; Continuous feature; Discrete feature; Mixture of continuous and discrete features
资金
- Humanities and Social Sciences Research Youth Foundation of Ministry of Education of China [14YJC870021]
- National Natural Science Foundation of China [61202271]
- Natural Science Foundation of Guangdong Province [S2013010013050]
- Major Program of National Social Science Foundation of China [12ZD222]
- National Social Science Fund of China [13CGL130]
- Guangdong Province Science and Technology Project [2014A040401083]
Feature selection is frequently used to reduce the number of features in many applications where data of high dimensionality are involved. Lots of the feature selection methods mainly focus on measuring the correlation (or similarity) between two features. However, most correlation measures are limited to handling only certain types of data. Feature space consisting of continuous/discrete feature or their combination presents a severe challenge to feature selection in terms of efficiency and effectiveness. This paper introduces a novel approach that can measure the correlation between a continuous and a discrete feature, and then proposes an efficient filter feature selection algorithm based on correlation analysis by removing weakly relevant and irrelevant features, as well as relevant but redundant features. Both theoretical and experimental comparisons with other representative filter approaches on UCI datasets show that the proposed algorithm is effective for selecting continuous and discrete features, as well as the mixture of continuous and discrete features. The performance of ECMBF is superior to other approaches in terms of dimensionality reduction and classification error rate. (C) 2015 Elsevier B.V. All rights reserved.
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