Optimizing multimodal feature selection using binary reinforced cuckoo search algorithm for improved classification performance
Background Feature selection is a vital process in data mining and machine learning approaches by determining which characteristics, out of the available features, are most appropriate for categorization or knowledge representation. However, the challenging task is finding a chosen subset of element...
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| Format: | Article |
| Language: | English |
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PeerJ Inc.
2024-01-01
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| Series: | PeerJ Computer Science |
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| Online Access: | https://peerj.com/articles/cs-1816.pdf |
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| author | Kalaipriyan Thirugnanasambandam Jayalakshmi Murugan Rajakumar Ramalingam Mamoon Rashid R. S. Raghav Tai-hoon Kim Gabriel Avelino Sampedro Mideth Abisado |
| author_facet | Kalaipriyan Thirugnanasambandam Jayalakshmi Murugan Rajakumar Ramalingam Mamoon Rashid R. S. Raghav Tai-hoon Kim Gabriel Avelino Sampedro Mideth Abisado |
| author_sort | Kalaipriyan Thirugnanasambandam |
| collection | DOAJ |
| description | Background Feature selection is a vital process in data mining and machine learning approaches by determining which characteristics, out of the available features, are most appropriate for categorization or knowledge representation. However, the challenging task is finding a chosen subset of elements from a given set of features to represent or extract knowledge from raw data. The number of features selected should be appropriately limited and substantial to prevent results from deviating from accuracy. When it comes to the computational time cost, feature selection is crucial. A feature selection model is put out in this study to address the feature selection issue concerning multimodal. Methods In this work, a novel optimization algorithm inspired by cuckoo birds’ behavior is the Binary Reinforced Cuckoo Search Algorithm (BRCSA). In addition, we applied the proposed BRCSA-based classification approach for multimodal feature selection. The proposed method aims to select the most relevant features from multiple modalities to improve the model’s classification performance. The BRCSA algorithm is used to optimize the feature selection process, and a binary encoding scheme is employed to represent the selected features. Results The experiments are conducted on several benchmark datasets, and the results are compared with other state-of-the-art feature selection methods to evaluate the effectiveness of the proposed method. The experimental results demonstrate that the proposed BRCSA-based approach outperforms other methods in terms of classification accuracy, indicating its potential applicability in real-world applications. In specific on accuracy of classification (average), the proposed algorithm outperforms the existing methods such as DGUFS with 32%, MBOICO with 24%, MBOLF with 29%, WOASAT 22%, BGSA with 28%, HGSA 39%, FS-BGSK 37%, FS-pBGSK 42%, and BSSA 40%. |
| first_indexed | 2024-03-08T09:16:29Z |
| format | Article |
| id | doaj.art-bca69eace9f94660b02025189ee5395c |
| institution | Directory Open Access Journal |
| issn | 2376-5992 |
| language | English |
| last_indexed | 2024-03-08T09:16:29Z |
| publishDate | 2024-01-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ Computer Science |
| spelling | doaj.art-bca69eace9f94660b02025189ee5395c2024-01-31T15:05:10ZengPeerJ Inc.PeerJ Computer Science2376-59922024-01-0110e181610.7717/peerj-cs.1816Optimizing multimodal feature selection using binary reinforced cuckoo search algorithm for improved classification performanceKalaipriyan Thirugnanasambandam0Jayalakshmi Murugan1Rajakumar Ramalingam2Mamoon Rashid3R. S. Raghav4Tai-hoon Kim5Gabriel Avelino Sampedro6Mideth Abisado7Centre for Smart Grid Technologies, School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, IndiaDepartment of Computer Science and Engineering, Kalasalingam Academy of Research and Education, Krishnankoil, IndiaCentre for Automation, School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, IndiaDepartment of Computer Engineering, Faculty of Science and Technology, Vishwakarma University, Pune, IndiaSchool of Computing, SASTRA Deemed University, Villupuram, IndiaSchool of Electrical and Computer Engineering, Chonnam National University, Daehak-7, Republic of KoreaFaculty of Information and Communication Studies, University of the Philippines Open University, Los Baños, PhilippinesCollege of Computing and Information Technologies, National University, Manila, PhilippinesBackground Feature selection is a vital process in data mining and machine learning approaches by determining which characteristics, out of the available features, are most appropriate for categorization or knowledge representation. However, the challenging task is finding a chosen subset of elements from a given set of features to represent or extract knowledge from raw data. The number of features selected should be appropriately limited and substantial to prevent results from deviating from accuracy. When it comes to the computational time cost, feature selection is crucial. A feature selection model is put out in this study to address the feature selection issue concerning multimodal. Methods In this work, a novel optimization algorithm inspired by cuckoo birds’ behavior is the Binary Reinforced Cuckoo Search Algorithm (BRCSA). In addition, we applied the proposed BRCSA-based classification approach for multimodal feature selection. The proposed method aims to select the most relevant features from multiple modalities to improve the model’s classification performance. The BRCSA algorithm is used to optimize the feature selection process, and a binary encoding scheme is employed to represent the selected features. Results The experiments are conducted on several benchmark datasets, and the results are compared with other state-of-the-art feature selection methods to evaluate the effectiveness of the proposed method. The experimental results demonstrate that the proposed BRCSA-based approach outperforms other methods in terms of classification accuracy, indicating its potential applicability in real-world applications. In specific on accuracy of classification (average), the proposed algorithm outperforms the existing methods such as DGUFS with 32%, MBOICO with 24%, MBOLF with 29%, WOASAT 22%, BGSA with 28%, HGSA 39%, FS-BGSK 37%, FS-pBGSK 42%, and BSSA 40%.https://peerj.com/articles/cs-1816.pdfReinforced cuckoo searchMultimodalBinary solution spaceFeature selectionMachine learningArtificial intelligence |
| spellingShingle | Kalaipriyan Thirugnanasambandam Jayalakshmi Murugan Rajakumar Ramalingam Mamoon Rashid R. S. Raghav Tai-hoon Kim Gabriel Avelino Sampedro Mideth Abisado Optimizing multimodal feature selection using binary reinforced cuckoo search algorithm for improved classification performance PeerJ Computer Science Reinforced cuckoo search Multimodal Binary solution space Feature selection Machine learning Artificial intelligence |
| title | Optimizing multimodal feature selection using binary reinforced cuckoo search algorithm for improved classification performance |
| title_full | Optimizing multimodal feature selection using binary reinforced cuckoo search algorithm for improved classification performance |
| title_fullStr | Optimizing multimodal feature selection using binary reinforced cuckoo search algorithm for improved classification performance |
| title_full_unstemmed | Optimizing multimodal feature selection using binary reinforced cuckoo search algorithm for improved classification performance |
| title_short | Optimizing multimodal feature selection using binary reinforced cuckoo search algorithm for improved classification performance |
| title_sort | optimizing multimodal feature selection using binary reinforced cuckoo search algorithm for improved classification performance |
| topic | Reinforced cuckoo search Multimodal Binary solution space Feature selection Machine learning Artificial intelligence |
| url | https://peerj.com/articles/cs-1816.pdf |
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