Binary Starling Murmuration Optimizer Algorithm to Select Effective Features from Medical Data

Binary Starling Murmuration Optimizer Algorithm to Select Effective Features from Medical Data

Feature selection is an NP-hard problem to remove irrelevant and redundant features with no predictive information to increase the performance of machine learning algorithms. Many wrapper-based methods using metaheuristic algorithms have been proposed to select effective features. However, they achi...

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Bibliographic Details
Main Authors: Mohammad H. Nadimi-Shahraki, Zahra Asghari Varzaneh, Hoda Zamani, Seyedali Mirjalili
Format: Article
Language:English
Published: MDPI AG 2022-12-01
Series:Applied Sciences
Subjects:
disease diagnosis
medical data
feature selection
binary metaheuristic algorithms
starling murmuration optimizer (SMO)
transfer function
Online Access:https://www.mdpi.com/2076-3417/13/1/564

Internet

https://www.mdpi.com/2076-3417/13/1/564

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