A Two-Stage Feature Selection Approach Based on Artificial Bee Colony and Adaptive LASSO in High-Dimensional Data
Year: 2024
Type: article
Abstract: 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.
Source: AppliedMath
Authors: Efe Precious Onakpojeruo, Nuriye Sancar
Institution: Near East University
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,137
OpenAlex ID: W4405329106