Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 37, 2020 No. 8
2320-2323,2337

Application of multi-objective optimization in feature selection subset evaluation

Wan Hong1a,1b,2
Li Mengmeng1a,1b,2
Wang Haofeng1a,1b,2
Yue Caitong1a,1b
Wang Li1a,1b,2
Shang Zhigang1a,1b,2
1. a. School of Electrical Engineering, b. Industrial Technology Research Institute, Zhengzhou University, Zhengzhou 450001, China
2. Henan Key Laboratory of Brain Science & Brain-Computer Interface Technology, Zhengzhou 450001, China
DOI: 10.19734/j.issn.1001-3695.2019.03.0043

Abstract

Feature selection is a common dimension reduction approach for processing high-dimensional big data, but it often involves multiple conflicting feature subsets evaluation objectives which are difficult to balance. To reach a compromise among various feature subset evaluations in feature selection and optimize the performance of subset, this paper proposed a subset evaluation multi-objective optimization based feature selection framework and focused on the application of multi-objective particle swarm optimization(MOPSO) in feature subset evaluation. The framework used sparsity, classification ability and information loss to design multi-objective optimization functions. Then it optimized the weight vectors of the features based on multi-objective optimization algorithm, and selected the "knee" of Pareto solution set as optimal vector. Finally, the framework realized feature selection based on weight vector ranking. This paper designed experiments to compare the performance of MOPSO based feature selection(FS_MOPSO) with four classical methods. The results on several standard data sets show that, FS_MOPSO shows higher classification accuracy in low dimensional space while ensuring less information loss.

Foundation Support

国家自然科学基金资助项目(U1304602,61673353)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2019.03.0043
Publish at: Application Research of Computers Printed Article, Vol. 37, 2020 No. 8
Section: Algorithm Research & Explore
Pages: 2320-2323,2337
Serial Number: 1001-3695(2020)08-016-2320-04

Publish History

[2020-08-05] Printed Article

Cite This Article

万红, 李蒙蒙, 王昊锋, 等. 多目标优化在特征选择子集评价中的应用 [J]. 计算机应用研究, 2020, 37 (8): 2320-2323,2337. (Wan Hong, Li Mengmeng, Wang Haofeng, et al. Application of multi-objective optimization in feature selection subset evaluation [J]. Application Research of Computers, 2020, 37 (8): 2320-2323,2337. )

About the Journal

  • 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.

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