Optimizing multimodal feature selection using binary reinforced cuckoo search algorithm for improved classification performance

Kalaipriyan Thirugnanasambandam; Jayalakshmi Murugan; Rajakumar Ramalingam; Mamoon Rashid; R S Raghav; Tai-Hoon Kim; Gabriel Avelino Sampedro; Mideth Abisado

doi:10.7717/peerj-cs.1816

Optimizing multimodal feature selection using binary reinforced cuckoo search algorithm for improved classification performance

PeerJ Comput Sci. 2024 Jan 29:10:e1816. doi: 10.7717/peerj-cs.1816. eCollection 2024.

Authors

Kalaipriyan Thirugnanasambandam¹, Jayalakshmi Murugan², Rajakumar Ramalingam³, Mamoon Rashid⁴, R S Raghav⁵, Tai-Hoon Kim⁶, Gabriel Avelino Sampedro^{7

8}, Mideth Abisado⁹

Affiliations

¹ Centre for Smart Grid Technologies, School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, India.
² Department of Computer Science and Engineering, Kalasalingam Academy of Research and Education, Krishnankoil, India.
³ Centre for Automation, School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, India.
⁴ Department of Computer Engineering, Faculty of Science and Technology, Vishwakarma University, Pune, India.
⁵ School of Computing, SASTRA Deemed University, Villupuram, India.
⁶ School of Electrical and Computer Engineering, Chonnam National University, Daehak-7, Republic of Korea.
⁷ Faculty of Information and Communication Studies, University of the Philippines Open University, Los Baños, Philippines.
⁸ Center for Computational Imaging and Visual Innovations, De La Salle University, Malate, Philippines.
⁹ College of Computing and Information Technologies, National University, Manila, Philippines.

Abstract

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%.

Keywords: Artificial intelligence; Binary solution space; Data science; Emerging technologies; Feature selection; Machine learning; Multimodal; Reinforced cuckoo search.

Grants and funding

The authors received no funding for this work.