B-MFO: A binary moth-flame optimization for feature selection from medical datasets

Mohammad H. Nadimi-Shahraki, Mahdis Banaie-Dezfouli, Hoda Zamani, Shokooh Taghian, Seyedali Mirjalili

Research output: Contribution to journalArticlepeer-review

43 Citations (Scopus)


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.

Original languageEnglish
Article number136
Issue number11
Publication statusPublished - Nov 2021


  • Binary metaheuristic algorithms
  • Feature selection
  • Medical datasets
  • Optimization
  • Swarm intelligence algorithms
  • Transfer function


Dive into the research topics of 'B-MFO: A binary moth-flame optimization for feature selection from medical datasets'. Together they form a unique fingerprint.

Cite this