A new hybrid feature selection approach using feature association map for supervised and unsupervised classification |
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| Affiliation: | 1. A.K. Choudhury School of Information Technology, University of Calcutta, Kolkata, India;2. Iwate Prefectural University, Japan;1. CINI Assistive Technologies National Lab & DAUIN, Politecnico di Torino, Italy;2. Department of Mathematics, University of Turin, Via Carlo Alberto 10, 10121 Torino, Italy;3. Istituto Superiore Mario Boella, Center for Applied Research on ICT, Via Pier Carlo Boggio 61, 10138, Torino, Italy;1. Department of Electronics and Communication Engineering, National Institute of Technology Goa, Farmagudi, Ponda, Goa, 403401, India;2. Department of Instrumentation and Control Engineering, PSG College of Technology, Coimbatore, 641004, India;3. Department of Electronics and Communication Engineering, Institute of Aeronautical Engineering, Dundigal, Hyderabad, 500 043, India;1. Advanced Virtual and Intelligent Computing (AVIC) Center, Department of Mathematics and Computer Science, Faculty of Science, Chulalongkorn University, Bangkok, Thailand;2. Department of Biochemistry, Faculty of Science, Chulalongkorn University, Bangkok, Thailand;3. Department of Microbiology, Faculty of Public Health, Mahidol University, Bangkok, Thailand;1. Department of Industrial Engineering, Islamic Azad University, Najafabad Branch, Najafabad, Iran;2. Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Australia;3. Electrical and Computer Engineering, Isfahan University of Technology, Isfahan 841583111, Iran;4. System Administrator, Dibrugarh University, Dibrugarh 786004, India;5. Department of Computer Engineering, Shahrekord University, Shahrekord 64165478, Iran;6. Department of Computer Engineering, Ferdowsi University of Mashhad, Mashhad 9177948974, Iran;7. School of Management & Enterprise, University of Southern Queensland, QLD 4300 Australia;8. Department of Information and Communications Technology, Faculty of Computer Science and Telecommunications, Cracow University of Technology, Warszawska 24 st., F-3, Krakow 31-155, Poland;9. Institute of Theoretical and Applied Informatics, Polish Academy of Sciences, Ba?tycka 5, Gliwice 44-100, Poland;10. Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore 599489, Singapore;11. Department of Biomedical Informatics and Medical Engineering, Asia University, Taichung, Taiwan;12. Department of Biomedical Engineering, School of Science and Technology, Singapore University of Social Sciences, Singapore;15. Faculty of Medicine, School of Population and Public Health, The University of British Columbia, Vancouver, Canada;1. Institute of Engineering and Management, Kolkata, India;2. University of Calcutta, Kolkata, India;3. Iwate Prefectural University, Japan |
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| Abstract: | Feature selection, both for supervised as well as for unsupervised classification is a relevant problem pursued by researchers for decades. There are multiple benchmark algorithms based on filter, wrapper and hybrid methods. These algorithms adopt different techniques which vary from traditional search-based techniques to more advanced nature inspired algorithm based techniques. In this paper, a hybrid feature selection algorithm using graph-based technique has been proposed. The proposed algorithm has used the concept of Feature Association Map (FAM) as an underlying foundation. It has used graph-theoretic principles of minimal vertex cover and maximal independent set to derive feature subset. This algorithm applies to both supervised and unsupervised classification. The performance of the proposed algorithm has been compared with several benchmark supervised and unsupervised feature selection algorithms and found to be better than them. Also, the proposed algorithm is less computationally expensive and hence has taken less execution time for the publicly available datasets used in the experiments, which include high-dimensional datasets. |
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