Title: Feature selection-based prediction model using binary dung beetle optimisation algorithm and improved CNN with regularisation model

Authors: R.S. Preyanka Lakshme; S. Ganesh Kumar

Addresses: Department of Data Science and Business Systems, SRM Institute of Science and Technology, Kattankulathur Campus, Tamil Nadu, India ' Department of Data Science and Business Systems, SRM Institute of Science and Technology, Kattankulathur Campus, Tamil Nadu, India

Abstract: Feature selection is a crucial process in data analysis, aiming to identify and eliminate irrelevant features to enhance model performance and efficiency. This research proposes a novel approach to address limitations in existing methodologies, such as overlooking feature interactions and struggling with large-scale datasets. Firstly, comprehensive data preprocessing steps including cleaning, transformation, and organisation are implemented to enhance data quality. Next, a binary version of the dung beetle optimisation algorithm is introduced to tackle feature selection issues, leveraging a U-shaped transfer function. Additionally, the dung beetle optimisation algorithm is augmented with Lévy flight to enhance solution diversity and a local search algorithm to prevent entrapment in local optima. The convolutional neural network is enhanced for prediction using dynamic chunk-based max pooling and dung beetle optimisation algorithms for layer improvement and weight refinement. Finally, to mitigate overfitting and optimise overall performance, a hybrid L_1/2 + L₂ regularisation model is applied.

Keywords: feature selection; Lévy flight; local search algorithm; improved CNN; dynamic chunk-based max pooling; DCMP.

DOI: 10.1504/IJIEI.2025.146688

International Journal of Intelligent Engineering Informatics, 2025 Vol.13 No.2, pp.195 - 227

Received: 26 Mar 2024
Accepted: 22 Jul 2024
Published online: 13 Jun 2025 *

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