期刊
PATTERN RECOGNITION LETTERS
卷 112, 期 -, 页码 70-74出版社
ELSEVIER SCIENCE BV
DOI: 10.1016/j.patrec.2018.06.005
关键词
Feature selection; Information theory; Classification; Composition of feature relevancy
资金
- National Key RD Plan of China [2017YFA0604500]
- National Sci-Tech Support Plan of China [2014BAH02F00]
- National Natural Science Foundation of China [61701190]
- Youth Science Foundation of Jilin Province of China [20160520011JH, 20180520021JH]
- Youth Sci-Tech Innovation Leader and Team Project of Jilin Province of China [20170519017JH]
- Key Technology Innovation Cooperation Project of Government and University for the whole Industry Demonstration [SXGJSF2017-4]
Feature selection plays a critical role in classification problems. Feature selection methods intend to retain relevant features and eliminate redundant features. This work focuses on feature selection methods based on information theory. By analyzing the composition of feature relevancy, we believe that a good feature selection method should maximize new classification information while minimizing feature redundancy. Therefore, a novel feature selection method named Composition of Feature Relevancy (CFR) is proposed. To evaluate CFR, we conduct experiments on eight real-world data sets and two different classifiers (Naive-Bayes and Support Vector Machine). Our method outperforms five other competing methods in terms of average classification accuracy and highest classification accuracy. (C) 2018 Elsevier B.V. All rights reserved.
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