- author = "Zhilei Zhou and Malcolm I. Heywood",
- title = "Gradient Boosted Programming for Low Cardinality Classification",
- journal = "ACM Transactions on Evolutionary Learning and Optimization",
- year = "2026",
- note = "Just Accepted",
- keywords = "genetic algorithms, genetic programming, Gradient boosting, classification",
- ISSN = "2688-299X",
-
URL = "
https://doi.org/10.1145/3742796",
-
DOI = "
10.1145/3742796",
- size = "26 pages",
- abstract = "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 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.",
-
notes = "https://dlnext.acm.org/journal/telo
Dalhousie University, Canada",
