Abstract
Feature selection and extraction play a key role in machine learning, helping to improve model performance and reduce data complexity. In the context of feature selection and extraction in machine learning, the Whales approach, also known as the Whale Optimization Algorithm (WOA), is often used. The unique properties of the Whales algorithm and its optimization capabilities can be useful in solving problems of feature selection and extraction. This article contains both a review of similar methods and an analysis of the Whales algorithm itself and the main stages of its application. The paper also examines the relationship between the Whales algorithm and traditional methods of feature selection and extraction and identifies future directions for research.
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