Abstract
Advancements in medical technology have created numerous large datasets including many features. Usually, all captured features are not necessary, and there are redundant and irrelevant features, which reduce the performance of algorithms. To tackle this challenge, many metaheuristic algorithms are used to select effective features. However, most of them are not effective and scalable enough to select effective features from large medical datasets as well as small ones. Therefore, in this paper, a binary moth-flame optimization (B-MFO) is proposed to select effective features from small and large medical datasets. Three categories of B-MFO were developed using S-shaped, V-shaped, and U-shaped transfer functions to convert the canonical MFO from continuous to binary. These categories of B-MFO were evaluated on seven medical datasets and the results were compared with four well-known binary metaheuristic optimization algorithms: BPSO, bGWO, BDA, and BSSA. In addition, the convergence behavior of the B-MFO and comparative algorithms were assessed, and the results were statistically analyzed using the Friedman test. The experimental results demonstrate a superior performance of B-MFO in solving the feature selection problem for different medical datasets compared to other comparative algorithms.
Subject
Computer Networks and Communications,Human-Computer Interaction
Reference109 articles.
1. A systematic approach for pre-processing electronic health records for mining: case study of heart disease
2. Knowledge discovery in medicine: Current issue and future trend
3. Classification of high dimensional biomedical data based on feature selection using redundant removal
4. A low cost model for diagnosing coronary artery disease based on effective features;Arjenaki;IJECCE,2015
5. Predict Survival of Patients with Lung Cancer Using an Ensemble Feature Selection Algotithm and Classification Methods in Data Mining;Dezfuly;J. Inf.,2015
Cited by
82 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献