Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 36, 2019 No. 7
1975-1979,2010

Feature selection algorithm based on filter+wrapper pattern

Zhou Chuanhua1,2
Liu Zhicai1
Ding Jing'an1
Zhou Jiayi3
1. School of Management Science & Engineering, Anhui University of Technology, Maanshan Anhui 243002, China
2. School of Computer Science & Technology, University of Science & Technology of China, Hefei 230026, China
3. Graduate School of Information, Production & Systems, Waseda University, Tokyo, Japan
DOI: 10.19734/j.issn.1001-3695.2018.01.0024

Abstract

Feature selection is one of the most important issues in data mining, machine learning and pattern recognition. Aiming at the problem of preference of traditional information gain algorithm in feature selection when the class and feature are unevenly distributed, this paper proposed a new feature selection algorithm based on information gain ratio and random forest. The proposed algorithm combined with the advantages of filter and wrapper modes. Firstly, it carried out a comprehensive measurement of features from two aspects of information correlation and classification ability. Secondly, it used sequential forward selection(SFS) strategy to select the features, and used the classification accuracy as the evaluation index to measure the feature subset. Finally, it obtained the optimal feature subset. The experimental results show that the proposed algorithm can not only achieve the effect of dimension reduction in feature space, but also effectively improve the classification performance and recall rate of classification algorithm.

Foundation Support

国家自然科学基金资助项目(71371013,71772002)
安徽省留学人员创新项目择优资助计划(2016)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2018.01.0024
Publish at: Application Research of Computers Printed Article, Vol. 36, 2019 No. 7
Section: Algorithm Research & Explore
Pages: 1975-1979,2010
Serial Number: 1001-3695(2019)07-012-1975-05

Publish History

[2019-07-05] Printed Article

Cite This Article

周传华, 柳智才, 丁敬安, 等. 基于filter+wrapper模式的特征选择算法 [J]. 计算机应用研究, 2019, 36 (7): 1975-1979,2010. (Zhou Chuanhua, Liu Zhicai, Ding Jing'an, et al. Feature selection algorithm based on filter+wrapper pattern [J]. Application Research of Computers, 2019, 36 (7): 1975-1979,2010. )

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  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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