Abstract
Organizations use fusion data modeling to integrate multiple data sources and build precise representations that achieve better organizational clarity. One recent method that has proven effective in many benchmark tests is the arithmetic optimization algorithm (AOA). AOA applies basic distribution behavior to arithmetic operations such as multiplication, division, addition, and subtraction. This paper focuses on the innovative application of AOA in addressing the feature selection problem. The binary version of this algorithm (BAOA) is introduced to solve problems of binary nature. The main part of this version is the transfer function that converts a continuous search space into a discrete search space. Therefore, a new Fountain-shaped transfer function is proposed to enhance global exploration and local exploitation in the BAOA algorithm. The performance of the proposed Fountain-shaped transfer function has been compared with V-shaped and S-shaped transfer functions. Based on ten public datasets, the performance of the proposed transfer function is validated. The Experimental results show the superiority of the proposed Fountain-shaped transfer function not only in getting high classification accuracy with few selected features but also requires inexpensive computational costs.
Published Version
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