Modified Filter Based Feature Selection Technique for Dermatology Dataset Using Beetle Swarm Optimization

J. Rajeshwari; M. Sughasiny

doi:10.4108/eetsis.vi.1998

Authors

J. Rajeshwari Research Scholar, Department of Computer Science, Srimad Andavan Arts and Science College, Tiruchirappalli, Tamil Nadu 620005, India
M. Sughasiny Assistant Professor, Department of Computer Science, Srimad Andavan Arts and Science College, Tiruchirappalli, Tamil Nadu 620005, India

DOI:

https://doi.org/10.4108/eetsis.vi.1998

Keywords:

Skin cancer, Feature selection algorithm, LSI, CFS, Beetle swarm optimization, Classification performance

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%.

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Dataset Link: https://archive.ics.uci.edu/ml/datasets/dermatology