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Adaptive Hybrid Metaheuristic Optimization Framework for Enhanced Feature Selection and Classification in High-Dimensional Biomedical Datasets

Authors: Akash Verma

Published: 2025-05-02 10:38:17.286

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Abstract

The analysis of high-dimensional biomedical datasets presents significant challenges due to the curse of dimensionality, leading to increased computational complexity and reduced classification accuracy. Feature selection, a crucial preprocessing step, aims to identify a subset of relevant features, thereby mitigating these issues. This paper proposes an Adaptive Hybrid Metaheuristic Optimization Framework (AHMOF) for feature selection and classification in high-dimensional biomedical datasets. AHMOF synergistically integrates the strengths of Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) with an adaptive control mechanism to dynamically adjust the balance between exploration and exploitation. The framework employs a novel fitness function that considers both classification accuracy and the number of selected features. Experimental results on several benchmark biomedical datasets demonstrate that AHMOF consistently outperforms traditional feature selection methods and standalone metaheuristic algorithms in terms of classification accuracy, feature subset size, and computational efficiency. The adaptive nature of AHMOF allows it to effectively navigate the complex search space, leading to robust and generalizable feature subsets for improved biomedical data analysis.

Keywords

Feature Selection, Metaheuristics, Hybrid Optimization, Biomedical Datasets, Machine Learning, Classification Accuracy, Adaptive Algorithm, Swarm Intelligence, Evolutionary Computation, High-Dimensional Data

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How to Cite

Akash Verma, (2025-05-02 10:38:17.286). Adaptive Hybrid Metaheuristic Optimization Framework for Enhanced Feature Selection and Classification in High-Dimensional Biomedical Datasets. JANOLI International Journal of Artificial Intelligence and its Applications, Volume EOCMPeqBj5R9ZDur0Rlk, Issue 3.

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