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Analysis of institutional authors

Riyahi M.Author

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February 26, 2025
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Multiobjective whale optimization algorithm-based feature selection for intelligent systems

Publicated to:International Journal Of Intelligent Systems. 37 (11): 9037-9054 - 2022-01-01 37(11), DOI: 10.1002/int.22979

Authors: Riyahi M; Rafsanjani MK; Gupta BB; Alhalabi W

Affiliations

Department of Computer Science and Information Engineering; Asia University; Taichung; Taiwan; Lebanese American University; Beirut; Lebanon; Center for Interdisciplinary Research; UPES; Dehradun; India; Research and Innovation Department; Skyline Univers - Author
Department of Computer Science; Faculty of Mathematics and Computer; Shahid Bahonar University of Kerman; Kerman; Iran - Author
Department of Electrical and Computer Engineering; University of Miami; Florida; Coral Gables; United States - Author

Abstract

With regard to large dimensions of contemporary data sets and restricted computational time of intelligent systems, reducing the dimensions of data sets is necessary. Feature selection is a practical way to remove a set of redundant, irrelevant, and noisy features. In this way, the speed of decision-making procedure will be increased while the accuracy of decisions will be retained. To this end, numerous attentions have been attracted to the topic and consequently, extensive range of methods has been proposed. Regarding the goals of the feature selection concept, the proposed algorithms in this field must be fast and accurate. Therefore, this paper proposes a light meanwhile accurate algorithm to fulfill the mentioned goals. The presented algorithm takes the speed advantage of Whale Optimization Algorithm (WOA) to propose a novel feature selection method for intelligent systems. Moreover, to reach the goal of accuracy, the proposed strategy considers three important fitness objectives, namely, the number of selected features, the accuracy of classification, and information gain. The proposed scheme considers an accurate multiobjective fitness function instead of manipulating the basic algorithm. The reason is that improving the basic algorithms, WOA in our case, may lead to loading more computational complexity. Also, to make the proposed algorithm as light as possible, this paper considers K-nearest neighbor algorithm as the main classifier. The proposed light feature selection algorithm is run on different data sets. Experimental results prove that this algorithm is able to reduce the number of features meanwhile it retains, and in some cases even increases, the accuracy of classification. © 2022 Wiley Periodicals LLC.

Keywords

Accuracy of classificationsClassification (of information)Data setDecision makingFeature selectionFeatures selectionInformation gainIntelligent systemsK-near neighborK-nearest neighborLarge dimensionsMotion compensationMulti objectiveNearest neighbor searchNearest-neighbourOptimization algorithmsWhale optimization algorithm

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal International Journal Of Intelligent Systems due to its progression and the good impact it has achieved in recent years, according to the agency WoS (JCR), it has become a reference in its field. In the year of publication of the work, 2022, it was in position 36/145, thus managing to position itself as a Q1 (Primer Cuartil), in the category Computer Science, Artificial Intelligence.

From a relative perspective, and based on the normalized impact indicator calculated from the Field Citation Ratio (FCR) of the Dimensions source, it yields a value of: 5.42, which indicates that, compared to works in the same discipline and in the same year of publication, it ranks as a work cited above average. (source consulted: Dimensions Jul 2025)

Specifically, and according to different indexing agencies, this work has accumulated citations as of 2025-07-27, the following number of citations:

  • Scopus: 13

Impact and social visibility

From the perspective of influence or social adoption, and based on metrics associated with mentions and interactions provided by agencies specializing in calculating the so-called "Alternative or Social Metrics," we can highlight as of 2025-07-27:

  • The use of this contribution in bookmarks, code forks, additions to favorite lists for recurrent reading, as well as general views, indicates that someone is using the publication as a basis for their current work. This may be a notable indicator of future more formal and academic citations. This claim is supported by the result of the "Capture" indicator, which yields a total of: 12 (PlumX).

Leadership analysis of institutional authors

This work has been carried out with international collaboration, specifically with researchers from: Iran; Lebanon; United States of America.

There is a significant leadership presence as some of the institution’s authors appear as the first or last signer, detailed as follows: First Author (RIYAHI, MILAD) .