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
Published in:	Applied sciences Vol. 13; no. 1; p. 564
Main Authors:	Nadimi-Shahraki, Mohammad H., Asghari Varzaneh, Zahra, Zamani, Hoda, Mirjalili, Seyedali
Format:	Journal Article
Language:	English
Published:	Basel MDPI AG 01.01.2023
Subjects:	Accuracy Artificial intelligence binary metaheuristic algorithms Classification Coronaviruses Data mining Datasets Diabetes Disease disease diagnosis Feature selection Hepatitis Machine learning medical data Medical diagnosis Mortality Optimization algorithms starling murmuration optimizer (SMO) transfer function
ISSN:	2076-3417, 2076-3417
Online Access:	Get full text
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