Research Article
Modified Filter Based Feature Selection Technique for Dermatology Dataset Using Beetle Swarm Optimization
@ARTICLE{10.4108/eetsis.vi.1998, author={J. Rajeshwari and M. Sughasiny}, title={Modified Filter Based Feature Selection Technique for Dermatology Dataset Using Beetle Swarm Optimization}, journal={EAI Endorsed Transactions on Scalable Information Systems}, volume={10}, number={2}, publisher={EAI}, journal_a={SIS}, year={2022}, month={7}, keywords={Skin cancer, Feature selection algorithm, LSI, CFS, Beetle swarm optimization, Classification performance}, doi={10.4108/eetsis.vi.1998} }- J. Rajeshwari
M. Sughasiny
Year: 2022
Modified Filter Based Feature Selection Technique for Dermatology Dataset Using Beetle Swarm Optimization
SIS
EAI
DOI: 10.4108/eetsis.vi.1998
Abstract
INTRODUCTION: Skin cancer is an emerging disease all over the world which causes a huge mortality. To detect skin cancer at an early stage, computer aided systems is designed. The most crucial step in it is the feature selection process because of its greater impact on classification performance. Various feature selection algorithms were designed previously to find the relevant features from a set of attributes. Yet, there arise challenges in selecting appropriate features from datasets related to disease prediction. OBJECTIVES: To design a hybrid feature selection algorithm for selecting relevant feature subspace from dermatology datasets. METHODS: The hybrid feature selection algorithm is designed by integrating the Latent Semantic Index (LSI) along with correlation-based Feature Selection (CFS). To achieve an optimal selection of feature subset, beetle swarm optimization is used. RESULTS: Statistical metrics such as accuracy, specificity, recall, F1 score and MCC are calculated. CONCLUSION: The accuracy and sensitivity value obtained is 95% and 92%.
Copyright © 2022 J. Rajeshwari et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution license, which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.
