Gradient Boosted Programming for Low Cardinality Classification

doi:10.1145/3742796

Gradient Boosted Programming for Low Cardinality Classification

2026-03-01

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DOI：10.1145/3742796

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Gradient boosting represents an effective approach for constructing ensembles. We demonstrate how genetic programming can take advantage of the method for a wide range of classification tasks. The resulting Gradient Boosted Programming approach assumes two phases. Phase 1 develops a diverse set of base learners (programs). Phase 2 applies a gradient boosting approach specific to the program representation. The resulting ensemble is additively constructed and a class probability distribution is learnt for each program. An extensive benchmarking study is conducted across 21 classification datasets that include requirements for operation under class imbalance, tens of classes, and feature identification. The proposed approach is significantly better under the 11 low cardinality classification tasks and generally identifies simpler models than other ensemble methods such as Random Forests and XGBoost.

Keywords:

Gradient boosting

genetic programming

classification

Gradient Boosted Programming for Low Cardinality Classification

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