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

IF 16.4 1区化学 Q1 CHEMISTRY, MULTIDISCIPLINARY

Accounts of Chemical Research Pub Date : 2022-07-15 DOI:10.4108/eetsis.vi.1998

J. Rajeshwari, M. Sughasiny, Researc H Article

引用次数: 4

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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基于甲虫群优化的皮肤病学数据集改进滤波特征选择技术

简介:皮肤癌是一种新兴疾病，在世界范围内造成巨大的死亡率。为了在早期发现皮肤癌，设计了计算机辅助系统。其中最关键的一步是特征选择过程，因为它对分类性能的影响较大。以前设计了各种特征选择算法来从一组属性中找到相关的特征。然而，在从与疾病预测相关的数据集中选择适当的特征方面存在挑战。目的:设计一种混合特征选择算法，用于从皮肤病学数据集中选择相关特征子空间。方法:将潜在语义索引(LSI)和基于关联的特征选择(CFS)相结合，设计混合特征选择算法。为了实现特征子集的最优选择，采用了甲虫群算法。结果:计算了准确性、特异性、召回率、F1评分和MCC等统计指标。结论:获得的准确度和灵敏度分别为95%和92%。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Accounts of Chemical Research 化学-化学综合

CiteScore

31.40

自引率

1.10%

发文量

312

审稿时长

2 months

期刊介绍： Accounts of Chemical Research presents short, concise and critical articles offering easy-to-read overviews of basic research and applications in all areas of chemistry and biochemistry. These short reviews focus on research from the author’s own laboratory and are designed to teach the reader about a research project. In addition, Accounts of Chemical Research publishes commentaries that give an informed opinion on a current research problem. Special Issues online are devoted to a single topic of unusual activity and significance. Accounts of Chemical Research replaces the traditional article abstract with an article "Conspectus." These entries synopsize the research affording the reader a closer look at the content and significance of an article. Through this provision of a more detailed description of the article contents, the Conspectus enhances the article's discoverability by search engines and the exposure for the research.