4.7 Article

Boosting Atomic Orbit Search Using Dynamic-Based Learning for Feature Selection

期刊

MATHEMATICS
卷 9, 期 21, 页码 -

出版社

MDPI
DOI: 10.3390/math9212786

关键词

soft computing; machine learning; feature selection (FS); metaheuristic (MH); atomic orbital search (AOS); dynamic opposite-based learning (DOL)

资金

  1. Hubei Provincial Science and Technology Major Project of China [2020AEA011]
  2. Key Research & Development Plan of Hubei Province of China [2020BAB100]
  3. project of Science, Technology and Innovation Commission of Shenzhen Municipality of China [JCYJ20210324120002006]

向作者/读者索取更多资源

Feature selection is a crucial step in soft computing and machine learning algorithms that aims to determine relevant features and improve data processing efficiency. Methods based on metaheuristic techniques have shown better performance compared to traditional methods, with the Atomic Orbital Search as a new approach in this domain.
Feature selection (FS) is a well-known preprocess step in soft computing and machine learning algorithms. It plays a critical role in different real-world applications since it aims to determine the relevant features and remove other ones. This process (i.e., FS) reduces the time and space complexity of the learning technique used to handle the collected data. The feature selection methods based on metaheuristic (MH) techniques established their performance over all the conventional FS methods. So, in this paper, we presented a modified version of new MH techniques named Atomic Orbital Search (AOS) as FS technique. This is performed using the advances of dynamic opposite-based learning (DOL) strategy that is used to enhance the ability of AOS to explore the search domain. This is performed by increasing the diversity of the solutions during the searching process and updating the search domain. A set of eighteen datasets has been used to evaluate the efficiency of the developed FS approach, named AOSD, and the results of AOSD are compared with other MH methods. From the results, AOSD can reduce the number of features by preserving or increasing the classification accuracy better than other MH techniques.

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