B-MFO: A Binary Moth-Flame Optimization for Feature Selection from Medical Datasets

Computers

Seyedali Mirjalili¹  Mohammad H. Nadimi-Shahraki²  Shokooh Taghian²  Hoda Zamani²  Mahdis Banaie-Dezfouli² 

[1] Centre for Artificial Intelligence Research and Optimisation, Torrens University Australia, Adelaide 5000, Australia;Faculty of Computer Engineering, Najafabad Branch, Islamic Azad University, Najafabad 8514143131, Iran;

关键词: optimization;    binary metaheuristic algorithms;    swarm intelligence algorithms;    feature selection;    medical datasets;    transfer function;   

DOI  :  10.3390/computers10110136

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown