Bio-Inspired Feature Selection: An Improved Binary Particle Swarm Optimization Approach

被引:61
作者
Ji, Bai [1 ]
Lu, Xiaozheng [1 ]
Sun, Geng [2 ,3 ]
Zhang, Wei [1 ]
Li, Jiahui [2 ]
Xiao, Yinzhe [2 ]
机构
[1] Jilin Univ, Hosp 1, Dept Hepatobiliary & Pancreat Surg, Changchun 130021, Peoples R China
[2] Jilin Univ, Coll Comp Sci & Technol, Changchun 130012, Peoples R China
[3] Jilin Univ, Key Lab Symbol Computat & Knowledge Engn, Minist Educ, Changchun 130012, Peoples R China
基金
中国国家自然科学基金; 中国博士后科学基金;
关键词
Feature selection; classification; bio-inspired computing; particle swarm optimization; GENETIC ALGORITHM; DIFFERENTIAL EVOLUTION; PSO; INFORMATION; COLONY; FILTER;
D O I
10.1109/ACCESS.2020.2992752
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Feature selection is an effective approach to reduce the number of features of data, which enhances the performance of classification in machine learning. In this paper, we formulate a joint feature selection problem to reduce the number of the selected features while enhancing the accuracy. An improved binary particle swarm optimization (IBPSO) algorithm is proposed to solve the formulated problem. IBPSO introduces a local search factor based on L & x00E9;vy flight, a global search factor based on weighting inertia coefficient, a population diversity improvement factor based on mutation mechanism and a binary mechanism to improve the performance of conventional PSO and to make it suitable for the binary feature selection problems. Experiments based on 16 classical datasets are selected to test the effectiveness of the proposed IBPSO algorithm, and the results demonstrate that IBPSO has better performance than some other comparison algorithms.
引用
收藏
页码:85989 / 86002
页数:14
相关论文
共 57 条
[1]   Modified cuckoo search algorithm with rough sets for feature selection [J].
Abd El Aziz, Mohamed ;
Hassanien, Aboul Ella .
NEURAL COMPUTING & APPLICATIONS, 2018, 29 (04) :925-934
[2]   A new fusion of grey wolf optimizer algorithm with a two-phase mutation for feature selection [J].
Abdel-Basset, Mohamed ;
El-Shahat, Doaa ;
El-henawy, Ibrahim ;
de Albuquerque, Victor Hugo C. ;
Mirjalili, Seyedali .
EXPERT SYSTEMS WITH APPLICATIONS, 2020, 139
[3]   A modified nature inspired meta-heuristic whale optimization algorithm for solving 0-1 knapsack problem [J].
Abdel-Basset, Mohamed ;
El-Shahat, Doaa ;
Sangaiah, Arun Kumar .
INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS, 2019, 10 (03) :495-514
[4]  
Al-Tashi Q, 2020, ALGO INTELL SY, P273, DOI 10.1007/978-981-32-9990-0_13
[5]   Binary Optimization Using Hybrid Grey Wolf Optimization for Feature Selection [J].
Al-Tashi, Qasem ;
Kadir, Said Jadid Abdul ;
Rais, Helmi Md ;
Mirjalili, Seyedali ;
Alhussian, Hitham .
IEEE ACCESS, 2019, 7 :39496-39508
[6]   Breast cancer diagnosis using GA feature selection and Rotation Forest [J].
Alickovic, Emina ;
Subasi, Abdulhamit .
NEURAL COMPUTING & APPLICATIONS, 2017, 28 (04) :753-763
[7]  
[Anonymous], 2012, COMPUTATIONAL COMPLE
[8]  
[Anonymous], 1998, TECH REP
[9]   A robust swarm intelligence-based feature selection model for neuro-fuzzy recognition of mild cognitive impairment from resting-state fMRI [J].
Anter, Ahmed M. ;
Wei, Yichen ;
Su, Jiahui ;
Yuan, Yueming ;
Lei, Beiying ;
Duan, Gaoxiong ;
Mai, Wei ;
Nong, Xiucheng ;
Yu, Bihan ;
Li, Chong ;
Fu, Zening ;
Zhao, Lihua ;
Deng, Demao ;
Zhang, Zhiguo .
INFORMATION SCIENCES, 2019, 503 :670-687
[10]   Merit-guided dynamic feature selection filter for data streams [J].
Barddal, Jean Paul ;
Enembreck, Fabricio ;
Gomes, Heitor Murilo ;
Bifet, Albert ;
Pfahringer, Bernhard .
EXPERT SYSTEMS WITH APPLICATIONS, 2019, 116 :227-242