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...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Journal of bionics engineering Ročník 8; číslo 2; s. 191 - 200
Hlavní autoři: Liu, Yuanning, Wang, Gang, Chen, Huiling, Dong, Hao, Zhu, Xiaodong, Wang, Sujing
Médium: Journal Article
Jazyk:angličtina
Vydáno: Singapore Elsevier Ltd 01.06.2011
Springer Singapore
Témata:
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
support vector machines
particle swarm optimization
data mining
feature selection
ISSN:1672-6529, 2543-2141
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí: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.
Bibliografie: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
ISSN:1672-6529
2543-2141
DOI:10.1016/S1672-6529(11)60020-6