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A Machine Learning-Based Hybrid Approach to Subset Selection Using Binary Ant Colony Optimization Functions

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Abstract

Data from many real-world applications is highly dimensional and the features of such data are highly redundant. These data require a lot of time and resources to process, making it difficult to extract relevant information. In data mining, selecting features is most important processes. By removing unnecessary or redundant information, feature selection lowers the dataset’s dimensionality and enhances classification performance. Ant Colony Optimization (ACO) is currently used in feature selection techniques that are applied to low-dimensional datasets. This work proposes a new algorithm called "Binary ACO Hybrid Approach for Feature Subset Selection" (BACOHAFSS). It seeks to choose the best feature subset with minimal redundancy. This algorithm is a binary connected graph, where each node represents a feature. Each function is a two-mode binary bit. 1 and 0 select 1 feature, 0 does not select a feature. On three datasets from the UCI repository, the suggested technique is evaluated and contrasted with the most recent systems. The experimental findings supported the BACOHAFSS’s claims that it improves classification accuracy and recovers the best subset of features.

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  1. Thanthai Periyar Govt. Arts & Science College (A), Affiliated to Bharathidasan University, Tiruchirappalli, India

    R. Senthamil Selvi & K. Fathima Bibi

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  1. R. Senthamil Selvi
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  2. K. Fathima Bibi
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Correspondence to R. Senthamil Selvi.

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This article is part of the topical collection “Advances in Computational Approaches for Image Processing, Wireless Networks, Cloud Applications and Network Security” guest edited by P. Raviraj, Maode Ma and Roopashree H R.

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Selvi, R.S., Bibi, K.F. A Machine Learning-Based Hybrid Approach to Subset Selection Using Binary Ant Colony Optimization Functions. SN COMPUT. SCI. 4, 853 (2023). https://doi.org/10.1007/s42979-023-02251-9

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