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A moving-horizon differential evolution algorithm for training deep classification trees on large datasets
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AI
DOI：10.1016/j.eswa.2026.133846.png)
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Abstract
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Decision trees are widely used in interpretable expert systems because their if-then rules can be inspected by domain experts. However, training accurate decision trees on large continuous-feature datasets remains difficult. Greedy methods such as CART scale well but optimize each split locally, while exact optimal-tree methods can produce high-quality trees only for relatively small datasets or shallow depths. Existing evolutionary approaches are more flexible, but full-tree search becomes increasingly difficult as tree depth grows. This creates a need for an optimization method that improves decision-tree accuracy for deep trees and large datasets. This paper presents the Moving-Horizon Differential Evolution algorithm for Optimal Classification Trees (MH-DEOCT). The proposed method combines three components: a moving-horizon decomposition that replaces full-tree optimization with a sequence of tractable subtree problems, a discrete tree decoding strategy that removes redundant threshold searches, and a GPU-accelerated fitness evaluation exploiting Single Instruction Multiple Data parallelism. Extensive experiments on 65 benchmark datasets show that MH-DEOCT trains depth-8 trees on datasets with up to 60 million samples, improves average test accuracy over CART by 2.73%, and exceeds DL8.5 by 2.27% on medium and large datasets. On a 60-million-sample wearable stress detection dataset, it improves test accuracy over CART by 5.64% at depth 8. Overall, these results show that MH-DEOCT improves predictive accuracy for deep decision trees on large datasets while retaining the transparency of the final tree.
Keywords:
Decision tree
Classification
Scalability
Optimization
GPU
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