Binary Social Mimic Optimization Algorithm With X-Shaped Transfer Function for Feature Selection
Definitive optimization algorithms are not able to solve high dimensional optimization problems when the search space grows exponentially with the problem size, and an exhaustive search also becomes impractical. To encounter this problem, researchers use approximation algorithms. A category of appro...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9098869/ |
| _version_ | 1818736596517650432 |
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| author | Kushal Kanti Ghosh Pawan Kumar Singh Junhee Hong Zong Woo Geem Ram Sarkar |
| author_facet | Kushal Kanti Ghosh Pawan Kumar Singh Junhee Hong Zong Woo Geem Ram Sarkar |
| author_sort | Kushal Kanti Ghosh |
| collection | DOAJ |
| description | Definitive optimization algorithms are not able to solve high dimensional optimization problems when the search space grows exponentially with the problem size, and an exhaustive search also becomes impractical. To encounter this problem, researchers use approximation algorithms. A category of approximation algorithms is meta-heuristic algorithms which have shown an acceptable degree of efficiency to solve this kind of problems. Social Mimic Optimization (SMO) algorithm is a recently proposed meta-heuristic algorithm which is used to optimize problems with continuous solution space. It is proposed by following the behavior of people in society. SMO can efficiently explore the solution space for obtaining optimal or near-optimal solution by minimizing a given fitness function. Feature selection is a binary optimization problem where the aim is to maximize the classification accuracy of a learning algorithm using minimum the number of features. To convert the continuous search space to a binary one, a proper transfer function is required. The effect a transfer function has on the binary variant of an optimization algorithm is very important since selecting a particular subset of features based on the solution values attained by the algorithm in continuous search space depends on the considered transfer function. To this end, we have proposed a new transfer function, namely X-shaped transfer function, to enhance the exploration and exploitation ability of binary SMO. The proposed X-shaped transfer function utilizes two components and crossover operation to obtain a new solution. Effect of the proposed X-shaped transfer function is compared with the effect of four S-shaped and four V-shaped transfer functions on SMO in terms of achieved classification accuracy, rate of convergence, and number of features selected over 18 standard UCI datasets. The proposed algorithm is also compared with state-of-the-art meta-heuristic feature selection (FS) algorithms. Experimental results confirm the efficiency of the proposed approach in improving the classification accuracy compared to other meta-heuristic algorithms, and the superiority of X-shaped transfer function over commonly used S-shaped and V-shaped transfer functions. The source code of the proposed method along with the datasets used can be found at https://github.com/Rangerix/SocialMimic. |
| first_indexed | 2024-12-18T00:39:40Z |
| format | Article |
| id | doaj.art-d119a6a0d3444b358439c05b82269ce2 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-18T00:39:40Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-d119a6a0d3444b358439c05b82269ce22022-12-21T21:26:56ZengIEEEIEEE Access2169-35362020-01-018978909790610.1109/ACCESS.2020.29966119098869Binary Social Mimic Optimization Algorithm With X-Shaped Transfer Function for Feature SelectionKushal Kanti Ghosh0Pawan Kumar Singh1Junhee Hong2https://orcid.org/0000-0003-1285-1454Zong Woo Geem3https://orcid.org/0000-0002-0370-5562Ram Sarkar4Department of Computer Science and Engineering, Jadavpur University, Kolkata, IndiaDepartment of Information Technology, Jadavpur University, Kolkata, IndiaDepartment of Energy IT, Gachon University, Seongnam, South KoreaDepartment of Energy IT, Gachon University, Seongnam, South KoreaDepartment of Computer Science and Engineering, Jadavpur University, Kolkata, IndiaDefinitive optimization algorithms are not able to solve high dimensional optimization problems when the search space grows exponentially with the problem size, and an exhaustive search also becomes impractical. To encounter this problem, researchers use approximation algorithms. A category of approximation algorithms is meta-heuristic algorithms which have shown an acceptable degree of efficiency to solve this kind of problems. Social Mimic Optimization (SMO) algorithm is a recently proposed meta-heuristic algorithm which is used to optimize problems with continuous solution space. It is proposed by following the behavior of people in society. SMO can efficiently explore the solution space for obtaining optimal or near-optimal solution by minimizing a given fitness function. Feature selection is a binary optimization problem where the aim is to maximize the classification accuracy of a learning algorithm using minimum the number of features. To convert the continuous search space to a binary one, a proper transfer function is required. The effect a transfer function has on the binary variant of an optimization algorithm is very important since selecting a particular subset of features based on the solution values attained by the algorithm in continuous search space depends on the considered transfer function. To this end, we have proposed a new transfer function, namely X-shaped transfer function, to enhance the exploration and exploitation ability of binary SMO. The proposed X-shaped transfer function utilizes two components and crossover operation to obtain a new solution. Effect of the proposed X-shaped transfer function is compared with the effect of four S-shaped and four V-shaped transfer functions on SMO in terms of achieved classification accuracy, rate of convergence, and number of features selected over 18 standard UCI datasets. The proposed algorithm is also compared with state-of-the-art meta-heuristic feature selection (FS) algorithms. Experimental results confirm the efficiency of the proposed approach in improving the classification accuracy compared to other meta-heuristic algorithms, and the superiority of X-shaped transfer function over commonly used S-shaped and V-shaped transfer functions. The source code of the proposed method along with the datasets used can be found at https://github.com/Rangerix/SocialMimic.https://ieeexplore.ieee.org/document/9098869/Social mimic optimizationtransfer functionmeta-heuristicfeature selectionUCI |
| spellingShingle | Kushal Kanti Ghosh Pawan Kumar Singh Junhee Hong Zong Woo Geem Ram Sarkar Binary Social Mimic Optimization Algorithm With X-Shaped Transfer Function for Feature Selection IEEE Access Social mimic optimization transfer function meta-heuristic feature selection UCI |
| title | Binary Social Mimic Optimization Algorithm With X-Shaped Transfer Function for Feature Selection |
| title_full | Binary Social Mimic Optimization Algorithm With X-Shaped Transfer Function for Feature Selection |
| title_fullStr | Binary Social Mimic Optimization Algorithm With X-Shaped Transfer Function for Feature Selection |
| title_full_unstemmed | Binary Social Mimic Optimization Algorithm With X-Shaped Transfer Function for Feature Selection |
| title_short | Binary Social Mimic Optimization Algorithm With X-Shaped Transfer Function for Feature Selection |
| title_sort | binary social mimic optimization algorithm with x shaped transfer function for feature selection |
| topic | Social mimic optimization transfer function meta-heuristic feature selection UCI |
| url | https://ieeexplore.ieee.org/document/9098869/ |
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