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  • https://doi.org/10.1109/idaacs53288.2021.9660979Copy DOI Icon

A Hybrid Rice Optimization Algorithm with Ant System for Feature Selection

  • Sep 22, 2021
  • Zhiwei Ye +3 more
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Abstract

Feature selection is an important processing step of data mining and aims at selecting a subset with high quality. The feature subset is obtained by reducing the irrelevant and redundant features based on maintaining satisfactory accuracy. Many algorithms might be trapped in local optima on solving high-dimensional feature selection. Hybrid rice optimization (HRO) is a newly presented algorithm, its population is divided into three types such as the maintainer, restorer, and sterile line to simulate the breeding process of Chinese hybrid rice. Three main stages are designed to update the restorer and sterile line. However, the maintainer line is not updated in HRO, which degrades the performance of the algorithm on high-dimensional feature selection. In this paper, a hybrid model named HROAS is proposed for feature selection by combining HRO with ant system (AS). AS is embedded as an operator in HRO to update the maintainer line due to its unique updating mechanism. The performance of HROAS is evaluated on nine standard benchmark datasets and compared with several classic and novel algorithms. The experimental results demonstrate the outperformance of HROAS.

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