Detection of Multiple Optimized Feature Subsets Using Genetic Algorithm for ECG-based Identification
Detection of Multiple Optimized Feature Subsets Using Genetic Algorithm for ECG-based Identification |
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| © 2025 by IJETT Journal | ||
| Volume-73 Issue-7 |
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| Year of Publication : 2025 | ||
| Author : Mamata Pandey, Anup Kumar Keshri | ||
| DOI : 10.14445/22315381/IJETT-V73I7P126 | ||
How to Cite?
Mamata Pandey, Anup Kumar Keshri, "Detection of Multiple Optimized Feature Subsets Using Genetic Algorithm for ECG-based Identification," International Journal of Engineering Trends and Technology, vol. 73, no. 7, pp.328-338, 2025. Crossref, https://doi.org/10.14445/22315381/IJETT-V73I7P126
Abstract
Selecting the most relevant features remains a crucial challenge in optimizing classification accuracy and computational efficiency, especially in high-dimensional datasets. Most of the approaches select a single subset, claiming it to be the most optimal. However, diverse combinations of features may compete on computational overhead and model selection. Different feature subsets may highlight distinct aspects of the dataset, helping domain experts gain better insights. This paper presents a novel approach for detecting multiple optimized feature subsets using a novel Genetic Algorithm (GA) variant for ECG-based identification. The proposed method employs GA to identify multiple optimal feature combinations, improving both accuracy and robustness. Experimental evaluations on the ECG dataset demonstrate the effectiveness of the approach in selecting optimal feature subsets, leading to enhanced classification performance. The results indicate that the proposed method can significantly improve identification accuracy while reducing feature dimensionality, making it a viable solution for real-world applications.
Keywords
Genetic Algorithm, ECG-based identification, Multiobjective optimization, Multi-optima optimization, Niching.
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