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Multi-tree and multi-strategy iterative genetic programming algorithm for feature subset construction in high-dimensional data classification
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DOI：10.1007/s10489-026-07406-8.png)
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Abstract
En 中文
High-dimensional features are widely used in fields such as gene expression analysis and finance due to their ability to capture complex system information. However, high-dimensional data often exhibit redundancy, sparsity, and nonlinear relationships, which hinder effective feature construction and pose challenges for machine learning models. Dimensionality reduction is therefore essential to transform high-dimensional data into compact, discriminative representations. Although Genetic Programming (GP) has shown potential in feature subset construction, it still faces challenges in handling high-dimensional data: (1) random terminal node selection produces low-quality individuals; (2) fixed evolutionary strategies limit the discovery of informative features; and (3) the vast search space necessitates effective dimensionality reduction. To address these issues, a multi-tree and multi-strategy iterative genetic programming (MMSGP) algorithm is proposed for feature subset construction and classification. (1) A feature-importance-based terminal node selection improves individual quality. (2) A triple iterative strategy effectively guides evolution. (3) A dynamic dual-subset feature selection reduces the search space by adaptively focusing on valuable features. Experimental results across twelve high-dimensional datasets demonstrate that MMSGP achieves substantial performance improvements compared with five baseline methods. Specifically, MMSGP attains higher average balanced classification accuracy than all baselines on nine datasets and surpasses them in macro F1-score on ten datasets. Ablation studies further confirm the effectiveness of the proposed components.
Keywords:
Genetic Programming
Multi-Strategy Iteration
High-Dimensional Data Classification
Feature Subset Construction
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