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Modified Dominance-Based Soft Set Approach for Feature Selection

Modified Dominance-Based Soft Set Approach for Feature Selection
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Author(s): Jothi G. (Department of Computer Applications, Sona College of Arts and Science, Salem, Tamil Nadu, India), Hannah Inbarani H. (Periyar University, Salem, India), Ahmad Taher Azar (College of Computer and Information Sciences, Prince Sultan University, Riyadh, Saudi Arabia & Faculty of Computers and Artificial Intelligence, Benha University, Benha, Egypt), Khaled M. Fouad (Faculty of Information Technology and Computer Science, Nile University, Sheikh Zayed City, Giza, Egypt & Faculty of Computers and Artificial Intelligence, Benha University, Benha, Egypt) and Sahar Fawzy Sabbeh (College of Computer Science and Engineering, University of Jeddah, Jeddah, Saudi Arabia & Faculty of Computers and Artificial Intelligence, Benha University, Benha, Egypt)
Copyright: 2022
Volume: 14
Issue: 1
Pages: 20
Source title: International Journal of Sociotechnology and Knowledge Development (IJSKD)
Editor(s)-in-Chief: Lincoln Christopher Wood (University of Otago, New Zealand) and Brian J. Galli (Assistant Professor and Graduate Program Director, Master of Science in Engineering Management Industrial Engineering, Hofstra University, USA)
DOI: 10.4018/IJSKD.289036

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Abstract

Big data analysis applications in the field of medical image processing have recently increased rapidly. Feature reduction plays a significant role in eliminating irrelevant features and creating a successful research model for Big Data applications. Fuzzy clustering is used for the segment of the nucleus. Various features, including shape, texture, and color-based features, have been used to address the segmented nucleus. The Modified Dominance Soft Set Feature Selection Algorithm (MDSSA) is intended in this paper to determine the most important features for the classification of leukaemia images. The results of the MDSSA are evaluated using the variance analysis called ANOVA. In the dataset extracted function, the MDSSA selected 17 percent of the features that were more promising than the existing reduction algorithms. The proposed approach also reduces the time needed for further analysis of Big Data. The experimental findings confirm that the performance of the proposed reduction approach is higher than other approaches.

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