Enhancing classification with hybrid feature selection: A multi-objective genetic algorithm for high-dimensional data

Bohrer, Jonas da S.; Dorn, Márcio

doi:10.1016/j.eswa.2024.124518

Expert Systems with Applications

2024

DOI: 10.1016/j.eswa.2024.124518

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Enhancing classification with hybrid feature selection: A multi-objective genetic algorithm for high-dimensional data

Jonas da S. Bohrer,

Márcio Dorn

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Cited by 4 publications

References 41 publications

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Hybrid feature selection-based machine learning methods for thermal preference prediction in diverse seasons and building environments

Bai,

Dong,

Liu

2025

Building and Environment

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Hybrid feature selection-based machine learning methods for thermal preference prediction in diverse seasons and building environments

Bai,

Dong,

Liu

2025

Building and Environment

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UniBFS: A novel uniform-solution-driven binary feature selection algorithm for high-dimensional data

Ahadzadeh,

Abdar,

Foroumandi

et al. 2024

Swarm and Evolutionary Computation

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A Two-Stage Feature Selection Approach Based on Artificial Bee Colony and Adaptive LASSO in High-Dimensional Data

Onakpojeruo,

Sancar

2024

AppliedMath

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High-dimensional datasets, where the number of features far exceeds the number of observations, present significant challenges in feature selection and model performance. This study proposes a novel two-stage feature-selection approach that integrates Artificial Bee Colony (ABC) optimization with Adaptive Least Absolute Shrinkage and Selection Operator (AD_LASSO). The initial stage reduces dimensionality while effectively dealing with complex, high-dimensional search spaces by using ABC to conduct a global search for the ideal subset of features. The second stage applies AD_LASSO, refining the selected features by eliminating redundant features and enhancing model interpretability. The proposed ABC-ADLASSO method was compared with the AD_LASSO, LASSO, stepwise, and LARS methods under different simulation settings in high-dimensional data and various real datasets. According to the results obtained from simulations and applications on various real datasets, ABC-ADLASSO has shown significantly superior performance in terms of accuracy, precision, and overall model performance, particularly in scenarios with high correlation and a large number of features compared to the other methods evaluated. This two-stage approach offers robust feature selection and improves predictive accuracy, making it an effective tool for analyzing high-dimensional data.

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Enhancing classification with hybrid feature selection: A multi-objective genetic algorithm for high-dimensional data

Cited by 4 publications

References 41 publications

Hybrid feature selection-based machine learning methods for thermal preference prediction in diverse seasons and building environments

Hybrid feature selection-based machine learning methods for thermal preference prediction in diverse seasons and building environments

UniBFS: A novel uniform-solution-driven binary feature selection algorithm for high-dimensional data

A Two-Stage Feature Selection Approach Based on Artificial Bee Colony and Adaptive LASSO in High-Dimensional Data

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