期刊
EXPERT SYSTEMS WITH APPLICATIONS
卷 110, 期 -, 页码 11-19出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.eswa.2018.05.029
关键词
Feature selection; Information theory; Classification; Class-independent feature redundancy; Class-dependent feature redundancy
类别
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 is a preprocessing step in many application areas that are relevant to expert and intelligent systems, such as data mining and machine learning. Feature selection criteria that are based on information theory can be generally sorted into two categories. The criteria in the first group focus on minimizing feature redundancy, whereas those in the second group aim to maximize new classification information. However, both groups of feature evaluation criteria fail to balance the importance of feature redundancy and new classification information. Therefore, we propose a hybrid feature selection method named Minimal Redundancy-Maximal New Classification Information (MR-MNCI) that integrates the two groups of feature selection criteria. Moreover, according to the characteristics of the two groups of selection criteria, we adopt class-dependent feature redundancy and class-independent feature redundancy. To evaluate MR-MNCI, seven competing feature selection methods are compared with our method on 12 real-world data sets. Our method achieves the best classification performance in terms of average classification accuracy and highest classification accuracy. (C) 2018 Elsevier Ltd. All rights reserved.
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