An Improved Particle Swarm Optimization for Feature Selection
Particle Swarm Optimization (PSO) is a popular and bionic algorithm based on the social behavior associated with bird flocking for optimization problems. To maintain the diversity of swarms, a few studies of multi-swarm strategy have been reported. However, the competition among swarms, reservation...
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| Published in: | Journal of bionics engineering Vol. 8; no. 2; pp. 191 - 200 |
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| Main Authors: | , , , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Singapore
Elsevier Ltd
01.06.2011
Springer Singapore |
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| ISSN: | 1672-6529, 2543-2141 |
| Online Access: | Get full text |
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| Abstract | Particle Swarm Optimization (PSO) is a popular and bionic algorithm based on the social behavior associated with bird flocking for optimization problems. To maintain the diversity of swarms, a few studies of multi-swarm strategy have been reported. However, the competition among swarms, reservation or destruction of a swarm, has not been considered further. In this paper, we formulate four rules by introducing the mechanism for survival of the fittest, which simulates the competition among the swarms. Based on the mechanism, we design a modified Multi-Swarm PSO (MSPSO) to solve discrete problems, which consists of a number of sub-swarms and a multi-swarm scheduler that can monitor and control each sub-swarm using the rules. To further settle the feature selection problems, we propose an Improved Feature Selection (IFS) method by integrating MSPSO, Support Vector Machines (SVM) with F-score method. The IFS method aims to achieve higher generalization capability through performing kernel parameter optimization and feature selection simultaneously. The performance of the proposed method is compared with that of the standard PSO based, Genetic Algorithm (GA) based and the grid search based methods on 10 benchmark datasets, taken from UCI machine learning and StatLog databases. The numerical results and statistical analysis show that the proposed IFS method performs significantly better than the other three methods in terms of prediction accuracy with smaller subset of features. |
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| AbstractList | Particle Swarm Optimization (PSO) is a popular and bionic algorithm based on the social behavior associated with bird flocking for optimization problems. To maintain the diversity of swarms, a few studies of multi-swarm strategy have been reported. However, the competition among swarms, reservation or destruction of a swarm, has not been considered further. In this paper, we formulate four rules by introducing the mechanism for survival of the fittest, which simulates the competition among the swarms. Based on the mechanism, we design a modified Multi-Swarm PSO (MSPSO) to solve discrete problems, which consists of a number of sub-swarms and a multi-swarm scheduler that can monitor and control each sub-swarm using the rules. To further settle the feature selection problems, we propose an Improved Feature Selection (IFS) method by integrating MSPSO, Support Vector Machines (SVM) with F-score method. The IFS method aims to achieve higher generalization capability through performing kernel parameter optimization and feature selection simultaneously. The performance of the proposed method is compared with that of the standard PSO based, Genetic Algorithm (GA) based and the grid search based methods on 10 benchmark datasets, taken from UCI machine learning and StatLog databases. The numerical results and statistical analysis show that the proposed IFS method performs significantly better than the other three methods in terms of prediction accuracy with smaller subset of features. Particle Swarm Optimization （PSO） is a popular and bionic algorithm based on the social behavior associated with bird flocking for optimization problems. To maintain the diversity of swarms, a few studies of multi-swarm strategy have been reported. However, the competition among swarms, reservation or destruction of a swarm, has not been considered further. In this paper, we formulate four rules by introducing the mechanism for survival of the fittest, which simulates the competition among the swarms. Based on the mechanism, we design a modified Multi-Swarm PSO （MSPSO） to solve discrete problems, which consists of a number of sub-swarms and a multi-swarm scheduler that can monitor and control each sub-swarm using the rules. To further settle the feature selection problems, we propose an Improved Feature Selection （1FS） method by integrating MSPSO, Support Vector Machines （SVM） with F-score method. The IFS method aims to achieve higher generalization capa- bility through performing kernel parameter optimization and feature selection simultaneously. The performance of the proposed method is compared with that of the standard PSO based, Genetic Algorithm （GA） based and the grid search based mcthods on 10 benchmark datasets, taken from UCI machine learning and StatLog databases. The numerical results and statistical analysis show that the proposed IFS method performs significantly better than the other three methods in terms of prediction accuracy with smaller subset of features. |
| Author | Chen, Huiling Dong, Hao Wang, Gang Zhu, Xiaodong Liu, Yuanning Wang, Sujing |
| AuthorAffiliation | College of Computer Science and Technology, Jilin University, Changchun 130012, P. R. China Key Laboratory of S.vmbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012, P. R. China |
| Author_xml | – sequence: 1 givenname: Yuanning surname: Liu fullname: Liu, Yuanning organization: College of Computer Science and Technology, Jilin University, Changchun 130012, P. R. China – sequence: 2 givenname: Gang surname: Wang fullname: Wang, Gang organization: College of Computer Science and Technology, Jilin University, Changchun 130012, P. R. China – sequence: 3 givenname: Huiling surname: Chen fullname: Chen, Huiling organization: College of Computer Science and Technology, Jilin University, Changchun 130012, P. R. China – sequence: 4 givenname: Hao surname: Dong fullname: Dong, Hao organization: College of Computer Science and Technology, Jilin University, Changchun 130012, P. R. China – sequence: 5 givenname: Xiaodong surname: Zhu fullname: Zhu, Xiaodong email: zhuxiaodong.jlu@gmail.com organization: College of Computer Science and Technology, Jilin University, Changchun 130012, P. R. China – sequence: 6 givenname: Sujing surname: Wang fullname: Wang, Sujing organization: College of Computer Science and Technology, Jilin University, Changchun 130012, P. R. China |
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| Notes | particle swarm optimization, feature selection, data mining, support vector machines 22-1355/TB Particle Swarm Optimization （PSO） is a popular and bionic algorithm based on the social behavior associated with bird flocking for optimization problems. To maintain the diversity of swarms, a few studies of multi-swarm strategy have been reported. However, the competition among swarms, reservation or destruction of a swarm, has not been considered further. In this paper, we formulate four rules by introducing the mechanism for survival of the fittest, which simulates the competition among the swarms. Based on the mechanism, we design a modified Multi-Swarm PSO （MSPSO） to solve discrete problems, which consists of a number of sub-swarms and a multi-swarm scheduler that can monitor and control each sub-swarm using the rules. To further settle the feature selection problems, we propose an Improved Feature Selection （1FS） method by integrating MSPSO, Support Vector Machines （SVM） with F-score method. The IFS method aims to achieve higher generalization capa- bility through performing kernel parameter optimization and feature selection simultaneously. The performance of the proposed method is compared with that of the standard PSO based, Genetic Algorithm （GA） based and the grid search based mcthods on 10 benchmark datasets, taken from UCI machine learning and StatLog databases. The numerical results and statistical analysis show that the proposed IFS method performs significantly better than the other three methods in terms of prediction accuracy with smaller subset of features. ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
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| Snippet | Particle Swarm Optimization （PSO） is a popular and bionic algorithm based on the social behavior associated with bird flocking for optimization problems. To... Particle Swarm Optimization (PSO) is a popular and bionic algorithm based on the social behavior associated with bird flocking for optimization problems. To... |
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| SubjectTerms | Artificial Intelligence Biochemical Engineering Bioinformatics Biomaterials Biomedical Engineering and Bioengineering Biomedical Engineering/Biotechnology Bionics Competition data mining Engineering feature selection Genetic algorithms IFS方法 Mathematical models Optimization particle swarm optimization PSO Searching Support vector machines 优化问题 功能选择 支持向量机 特征选择 社会行为 粒子群优化 |
| Title | An Improved Particle Swarm Optimization for Feature Selection |
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