Improved Binary Sailfish Optimizer Based on Adaptive <italic>β</italic>-Hill Climbing for Feature Selection
Feature selection (FS), an important pre-processing step in the fields of machine learning and data mining, has immense impact on the outcome of the corresponding learning models. Basically, it aims to remove all possible irrelevant as well as redundant features from a feature vector, thereby enhanc...
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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/9082591/ |
| _version_ | 1818557473223606272 |
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| author | Kushal Kanti Ghosh Shameem Ahmed Pawan Kumar Singh Zong Woo Geem Ram Sarkar |
| author_facet | Kushal Kanti Ghosh Shameem Ahmed Pawan Kumar Singh Zong Woo Geem Ram Sarkar |
| author_sort | Kushal Kanti Ghosh |
| collection | DOAJ |
| description | Feature selection (FS), an important pre-processing step in the fields of machine learning and data mining, has immense impact on the outcome of the corresponding learning models. Basically, it aims to remove all possible irrelevant as well as redundant features from a feature vector, thereby enhancing the performance of the overall prediction or classification model. Over the years, meta-heuristic optimization techniques have been applied for FS, as these are able to overcome the limitations of traditional optimization approaches. In this work, we introduce a binary variant of the recently-proposed Sailfish Optimizer (SFO), named as Binary Sailfish (BSF) optimizer, to solve FS problems. Sigmoid transfer function is utilized here to map the continuous search space of SFO to a binary one. In order to improve the exploitation ability of the BSF optimizer, we amalgamate another recently proposed meta-heuristic algorithm, namely adaptive β-hill climbing (AβHC) with BSF optimizer. The proposed BSF and AβBSF algorithms are applied on 18 standard UCI datasets and compared with 10 state-of-the-art meta-heuristic FS methods. The results demonstrate the superiority of both BSF and AβBSF algorithms in solving FS problems. The source code of this work is available in https://github.com/Rangerix/MetaheuristicOptimization. |
| first_indexed | 2024-12-13T23:59:57Z |
| format | Article |
| id | doaj.art-b5843dde1cdb4e73b2f048c77a3965b5 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-13T23:59:57Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-b5843dde1cdb4e73b2f048c77a3965b52022-12-21T23:26:23ZengIEEEIEEE Access2169-35362020-01-018835488356010.1109/ACCESS.2020.29915439082591Improved Binary Sailfish Optimizer Based on Adaptive <italic>β</italic>-Hill Climbing for Feature SelectionKushal Kanti Ghosh0https://orcid.org/0000-0003-0929-5928Shameem Ahmed1https://orcid.org/0000-0003-1795-3361Pawan Kumar Singh2Zong Woo Geem3https://orcid.org/0000-0002-0370-5562Ram Sarkar4Department of Computer Science and Engineering, Jadavpur University, Kolkata, IndiaDepartment 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 Computer Science and Engineering, Jadavpur University, Kolkata, IndiaFeature selection (FS), an important pre-processing step in the fields of machine learning and data mining, has immense impact on the outcome of the corresponding learning models. Basically, it aims to remove all possible irrelevant as well as redundant features from a feature vector, thereby enhancing the performance of the overall prediction or classification model. Over the years, meta-heuristic optimization techniques have been applied for FS, as these are able to overcome the limitations of traditional optimization approaches. In this work, we introduce a binary variant of the recently-proposed Sailfish Optimizer (SFO), named as Binary Sailfish (BSF) optimizer, to solve FS problems. Sigmoid transfer function is utilized here to map the continuous search space of SFO to a binary one. In order to improve the exploitation ability of the BSF optimizer, we amalgamate another recently proposed meta-heuristic algorithm, namely adaptive β-hill climbing (AβHC) with BSF optimizer. The proposed BSF and AβBSF algorithms are applied on 18 standard UCI datasets and compared with 10 state-of-the-art meta-heuristic FS methods. The results demonstrate the superiority of both BSF and AβBSF algorithms in solving FS problems. The source code of this work is available in https://github.com/Rangerix/MetaheuristicOptimization.https://ieeexplore.ieee.org/document/9082591/Binary sailfish optimizerfeature selectionadaptive <italic xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">β</italic>-hill climbinghybrid optimizationUCI dataset |
| spellingShingle | Kushal Kanti Ghosh Shameem Ahmed Pawan Kumar Singh Zong Woo Geem Ram Sarkar Improved Binary Sailfish Optimizer Based on Adaptive <italic>β</italic>-Hill Climbing for Feature Selection IEEE Access Binary sailfish optimizer feature selection adaptive <italic xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">β</italic>-hill climbing hybrid optimization UCI dataset |
| title | Improved Binary Sailfish Optimizer Based on Adaptive <italic>β</italic>-Hill Climbing for Feature Selection |
| title_full | Improved Binary Sailfish Optimizer Based on Adaptive <italic>β</italic>-Hill Climbing for Feature Selection |
| title_fullStr | Improved Binary Sailfish Optimizer Based on Adaptive <italic>β</italic>-Hill Climbing for Feature Selection |
| title_full_unstemmed | Improved Binary Sailfish Optimizer Based on Adaptive <italic>β</italic>-Hill Climbing for Feature Selection |
| title_short | Improved Binary Sailfish Optimizer Based on Adaptive <italic>β</italic>-Hill Climbing for Feature Selection |
| title_sort | improved binary sailfish optimizer based on adaptive italic x03b2 italic hill climbing for feature selection |
| topic | Binary sailfish optimizer feature selection adaptive <italic xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">β</italic>-hill climbing hybrid optimization UCI dataset |
| url | https://ieeexplore.ieee.org/document/9082591/ |
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