B-MFO: A Binary Moth-Flame Optimization for Feature Selection from Medical Datasets
| Autor: | Hoda Zamani, Seyedali Mirjalili, Mohammad H. Nadimi-Shahraki, Mahdis Banaie-Dezfouli, Shokooh Taghian |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Computer Networks and Communications
Computer science business.industry Binary number Feature selection Pattern recognition QA75.5-76.95 medical datasets Transfer function Human-Computer Interaction feature selection binary metaheuristic algorithms Friedman test Metaheuristic algorithms Electronic computers. Computer science Convergence (routing) Scalability Moth flame optimization transfer function Artificial intelligence swarm intelligence algorithms business optimization |
| Zdroj: | Computers, Vol 10, Iss 136, p 136 (2021) Computers Volume 10 Issue 11 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|