Title: Improved artificial bee colony algorithm and its feature selection and evaluation based on granularity rough entropy and cloud model

Authors: Shouchun Yue; Lili Zhang

Addresses: School of Big Data, Chongqing Water Resources and Electric Engineering College, Chongqing, 402160, China ' School of Artificial Intelligence and Big Data, Chongqing College of Finance and Economics, Chongqing, 402160, China

Abstract: With the rise of the digital economy, data analysis is crucial. Current machine learning struggles with multi-feature data. An enhanced artificial bee colony algorithm improves feature selection. In the comparison of running time and loss value, the average running time was 1.25 s and the loss value was 0.13, which was significantly better than comparison algorithms. This result indicated that the algorithm was effective. In addition, in the comparative analysis of application effects, the algorithm performed better than other comparison algorithms in the selected feature subset size on different datasets. In the bearing fault dataset, it was found that the classification accuracy of this algorithm was 98.8%, significantly better than the comparison methods. The designed method has good performance and practical value, which is conducive to improving the accuracy and quality of data feature selection, and providing a certain theoretical basis for improving data analysis and theory.

Keywords: granularity roughness entropy; cloud model; ABC; feature selection; data.

DOI: 10.1504/IJCC.2025.151123

International Journal of Cloud Computing, 2025 Vol.14 No.4, pp.409 - 424

Received: 22 May 2025
Accepted: 18 Aug 2025
Published online: 14 Jan 2026 *

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