Abstract
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.
Highlights
Introduction conditions of the Creative CommonsData has become the backbones of different fields and domains in recent decades, such as artificial intelligence, data science, data mining, and other related fields
Extensive comparisons to several existing optimization methods are carried out to verify the performance of the developed AOSD method
This paper developed a modified Atomic Orbit Search (AOS) and used it as a feature selection (FS) approach
Summary
Data has become the backbones of different fields and domains in recent decades, such as artificial intelligence, data science, data mining, and other related fields. The problems of the high dimensionality and big size data have particular impacts on the machine learning classification techniques, represented by the high computational cost and decreasing the classification accuracy [1,2,3]. To solve such challenges, Dimensionality Reduction (DR). The enhanced AOS depends on using the dynamic opposite-based learning strategy to improve the exploration and maintain the diversity of solutions during the searching process. We propose an alternative feature selection method to improve the behavior of atomic Orbit optimization (AOS).
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
