Genetic programming evolves interpretable feature sets or full survival tree structures, improving performance over standard induction on two real-world datasets at two tree depths.
arXiv preprint arXiv:2509.22673 (2025)
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
cs.LG 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
Enhanced Baymex with parallelization and adaptive steering yields statistically similar or better classification performance than decision trees, logistic regression, naive Bayes and random forests on clinical data while returning multiple compact, inspectable Bayesian networks.
citing papers explorer
-
Evolving Features vs Evolving Entire Trees with GP for Interpretable Survival Analysis
Genetic programming evolves interpretable feature sets or full survival tree structures, improving performance over standard induction on two real-world datasets at two tree depths.
-
Parallel Adaptive Multi-Objective Evolutionary Learning of Discretized Bayesian Network Classifiers for Clinical Data
Enhanced Baymex with parallelization and adaptive steering yields statistically similar or better classification performance than decision trees, logistic regression, naive Bayes and random forests on clinical data while returning multiple compact, inspectable Bayesian networks.