Feature Selection and Parameter Optimization of Support Vector Machines Based on Modified Cat Swarm Optimization
Recently, applications of Internet of Things create enormous volumes of data, which are available for classification and prediction. Classification of big data needs an effective and efficient metaheuristic search algorithm to find the optimal feature subset. Cat swarm optimization (CSO) is a novel...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2015-07-01
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| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1155/2015/365869 |
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| _version_ | 1832547786041589760 |
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| author | Kuan-Cheng Lin Yi-Hung Huang Jason C. Hung Yung-Tso Lin |
| author_facet | Kuan-Cheng Lin Yi-Hung Huang Jason C. Hung Yung-Tso Lin |
| author_sort | Kuan-Cheng Lin |
| collection | DOAJ |
| description | Recently, applications of Internet of Things create enormous volumes of data, which are available for classification and prediction. Classification of big data needs an effective and efficient metaheuristic search algorithm to find the optimal feature subset. Cat swarm optimization (CSO) is a novel metaheuristic for evolutionary optimization algorithms based on swarm intelligence. CSO imitates the behavior of cats through two submodes: seeking and tracing. Previous studies have indicated that CSO algorithms outperform other well-known metaheuristics, such as genetic algorithms and particle swarm optimization. This study presents a modified version of cat swarm optimization (MCSO), capable of improving search efficiency within the problem space. The basic CSO algorithm was integrated with a local search procedure as well as the feature selection and parameter optimization of support vector machines (SVMs). Experiment results demonstrate the superiority of MCSO in classification accuracy using subsets with fewer features for given UCI datasets, compared to the original CSO algorithm. Moreover, experiment results show the fittest CSO parameters and MCSO take less training time to obtain results of higher accuracy than original CSO. Therefore, MCSO is suitable for real-world applications. |
| format | Article |
| id | doaj-art-7aa815168f6c4c3aaa5c53b4e3ec637a |
| institution | Kabale University |
| issn | 1550-1477 |
| language | English |
| publishDate | 2015-07-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj-art-7aa815168f6c4c3aaa5c53b4e3ec637a2025-02-03T06:43:18ZengWileyInternational Journal of Distributed Sensor Networks1550-14772015-07-011110.1155/2015/365869365869Feature Selection and Parameter Optimization of Support Vector Machines Based on Modified Cat Swarm OptimizationKuan-Cheng Lin0Yi-Hung Huang1Jason C. Hung2Yung-Tso Lin3 Department of Management Information Systems, National Chung Hsing University, Taichung 40227, Taiwan Department of Mathematics Education, National Taichung University of Education, Taichung 40306, Taiwan Department of Information Management, Overseas Chinese University, Taichung 40721, Taiwan Department of Management Information Systems, National Chung Hsing University, Taichung 40227, TaiwanRecently, applications of Internet of Things create enormous volumes of data, which are available for classification and prediction. Classification of big data needs an effective and efficient metaheuristic search algorithm to find the optimal feature subset. Cat swarm optimization (CSO) is a novel metaheuristic for evolutionary optimization algorithms based on swarm intelligence. CSO imitates the behavior of cats through two submodes: seeking and tracing. Previous studies have indicated that CSO algorithms outperform other well-known metaheuristics, such as genetic algorithms and particle swarm optimization. This study presents a modified version of cat swarm optimization (MCSO), capable of improving search efficiency within the problem space. The basic CSO algorithm was integrated with a local search procedure as well as the feature selection and parameter optimization of support vector machines (SVMs). Experiment results demonstrate the superiority of MCSO in classification accuracy using subsets with fewer features for given UCI datasets, compared to the original CSO algorithm. Moreover, experiment results show the fittest CSO parameters and MCSO take less training time to obtain results of higher accuracy than original CSO. Therefore, MCSO is suitable for real-world applications.https://doi.org/10.1155/2015/365869 |
| spellingShingle | Kuan-Cheng Lin Yi-Hung Huang Jason C. Hung Yung-Tso Lin Feature Selection and Parameter Optimization of Support Vector Machines Based on Modified Cat Swarm Optimization International Journal of Distributed Sensor Networks |
| title | Feature Selection and Parameter Optimization of Support Vector Machines Based on Modified Cat Swarm Optimization |
| title_full | Feature Selection and Parameter Optimization of Support Vector Machines Based on Modified Cat Swarm Optimization |
| title_fullStr | Feature Selection and Parameter Optimization of Support Vector Machines Based on Modified Cat Swarm Optimization |
| title_full_unstemmed | Feature Selection and Parameter Optimization of Support Vector Machines Based on Modified Cat Swarm Optimization |
| title_short | Feature Selection and Parameter Optimization of Support Vector Machines Based on Modified Cat Swarm Optimization |
| title_sort | feature selection and parameter optimization of support vector machines based on modified cat swarm optimization |
| url | https://doi.org/10.1155/2015/365869 |
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