A Top-Down Correlated Data Reduction Method in Feature Selection Process

Subject Areas : journal of Artificial Intelligence in Electrical Engineering

Mehdi Ayar ^{1 *}

1 - Department of Computer Engineering, Shab.C., Islamic Azad University, Shabestar, Iran

Received: 2025-05-20 Accepted : 2025-06-29 Published : 2025-09-09

Keywords: Correlated Features, Data Reduction, Feature Selection, Top-Down Method,

Abstract :

Feature selection is a main step in the data classification process, and it is applied to determine optimal features and develop an accurate model. This paper presents a novel top-down correlated data reduction method which is employed by feature selection process. The method removes redundant features by clustering and placing correlated features in a cluster. This operation continues until reaching a certain and desired number of features. The proposed feature selection method resulted in performance rates of 88.21%, 89.41%, 87.64%, and 86.54% in terms of accuracy, sensitivity, specificity, and f-measure, respectively which are highly effective compared to the current state-of-the-art approaches.

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