Optimal feature selection using binary teaching learning based optimization algorithm
Feature selection is a significant task in the workflow of predictive modeling for data analysis. Recent advanced feature selection methods are using the power of optimization algorithms for choosing a subset of relevant features to get better classification results. Most of the optimization algorit...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Elsevier
2022-02-01
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| Series: | Journal of King Saud University: Computer and Information Sciences |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1319157818306463 |
| _version_ | 1828863134339694592 |
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| author | Mohan Allam M. Nandhini |
| author_facet | Mohan Allam M. Nandhini |
| author_sort | Mohan Allam |
| collection | DOAJ |
| description | Feature selection is a significant task in the workflow of predictive modeling for data analysis. Recent advanced feature selection methods are using the power of optimization algorithms for choosing a subset of relevant features to get better classification results. Most of the optimization algorithms like genetic algorithm use many controlling parameters which need to be tuned for better performance. Tuning these parameter values is a challenging task for the feature selection process. In this paper, we have developed a new wrapper-based feature selection method called binary teaching learning based optimization (FS-BTLBO) algorithm which needs only common controlling parameters like population size, and a number of generations to obtain a subset of optimal features from the dataset. We have used different classifiers as an objective function to compute the fitness of individuals for evaluating the efficiency of the proposed system. The results have proven that FS-BTLBO produces higher accuracy with a minimal number of features on Wisconsin diagnosis breast cancer (WDBC) data set to classify malignant and benign tumors. |
| first_indexed | 2024-12-13T03:37:45Z |
| format | Article |
| id | doaj.art-393c13e29f73425bae92dc5a85086795 |
| institution | Directory Open Access Journal |
| issn | 1319-1578 |
| language | English |
| last_indexed | 2024-12-13T03:37:45Z |
| publishDate | 2022-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of King Saud University: Computer and Information Sciences |
| spelling | doaj.art-393c13e29f73425bae92dc5a850867952022-12-22T00:01:00ZengElsevierJournal of King Saud University: Computer and Information Sciences1319-15782022-02-01342329341Optimal feature selection using binary teaching learning based optimization algorithmMohan Allam0M. Nandhini1Research Scholar, Pondicherry University, Puducherry, India; Corresponding author at: Department of Information Technology, Shri Vishnu Engineering College for Women, Bhimavaram, West Godavari District, Andhra Pradesh 534204, India.Department of Computer Science, Pondicherry University, Puducherry, IndiaFeature selection is a significant task in the workflow of predictive modeling for data analysis. Recent advanced feature selection methods are using the power of optimization algorithms for choosing a subset of relevant features to get better classification results. Most of the optimization algorithms like genetic algorithm use many controlling parameters which need to be tuned for better performance. Tuning these parameter values is a challenging task for the feature selection process. In this paper, we have developed a new wrapper-based feature selection method called binary teaching learning based optimization (FS-BTLBO) algorithm which needs only common controlling parameters like population size, and a number of generations to obtain a subset of optimal features from the dataset. We have used different classifiers as an objective function to compute the fitness of individuals for evaluating the efficiency of the proposed system. The results have proven that FS-BTLBO produces higher accuracy with a minimal number of features on Wisconsin diagnosis breast cancer (WDBC) data set to classify malignant and benign tumors.http://www.sciencedirect.com/science/article/pii/S1319157818306463Feature selectionBinary teaching learning based optimizationGenetic algorithmBreast cancer |
| spellingShingle | Mohan Allam M. Nandhini Optimal feature selection using binary teaching learning based optimization algorithm Journal of King Saud University: Computer and Information Sciences Feature selection Binary teaching learning based optimization Genetic algorithm Breast cancer |
| title | Optimal feature selection using binary teaching learning based optimization algorithm |
| title_full | Optimal feature selection using binary teaching learning based optimization algorithm |
| title_fullStr | Optimal feature selection using binary teaching learning based optimization algorithm |
| title_full_unstemmed | Optimal feature selection using binary teaching learning based optimization algorithm |
| title_short | Optimal feature selection using binary teaching learning based optimization algorithm |
| title_sort | optimal feature selection using binary teaching learning based optimization algorithm |
| topic | Feature selection Binary teaching learning based optimization Genetic algorithm Breast cancer |
| url | http://www.sciencedirect.com/science/article/pii/S1319157818306463 |
| work_keys_str_mv | AT mohanallam optimalfeatureselectionusingbinaryteachinglearningbasedoptimizationalgorithm AT mnandhini optimalfeatureselectionusingbinaryteachinglearningbasedoptimizationalgorithm |