A Two-Stage Feature Selection Approach Based on Artificial Bee Colony and Adaptive LASSO in High-Dimensional Data
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 Ad...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-12-01
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| Series: | AppliedMath |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2673-9909/4/4/81 |
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| Summary: | 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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| ISSN: | 2673-9909 |