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

EAI Endorsed Transactions on Scalable Information Systems

article

J. Rajeshwari¹  M. Sughasiny² 

[1] Research Scholar, Department of Computer Science, Srimad Andavan Arts and Science College;Assistant Professor, Department of Computer Science, Srimad Andavan Arts and Science College

关键词: Skin cancer;    Feature selection algorithm;    LSI;    CFS;    Beetle swarm optimization;    Classification performance;   

DOI  :  10.4108/eetsis.vi.1998

学科分类：社会科学、人文和艺术（综合）

来源: Bern Open Publishing

【 摘 要 】

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%.

【 授权许可】

CC BY