Indexado en

Licencia y uso

Citaciones

14

CITATIONS

14 Total citations

13 Recent citations

5.67 Field Citation Ratio

n/a Relative Citation Ratio

Cited 16 times in

Cited 9 times in

Altmetrics

Análisis de autorías institucional

Compartir

Multiobjective whale optimization algorithm-based feature selection for intelligent systems

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

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

Afiliaciones

Resumen

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.

Palabras clave