GA Tree: genetically evolved decision trees
By A. Papagelis, Dimitris Kalles. 2000
62 citations recorded by the source corpus.
We use genetic algorithms to evolve classification decision trees. The performance of the system is measured on a set of standard discretized concept learning problems and compared (very favorably) with the performance of two known algorithms (C4.5, OneR).
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- Cynthia Rudin
- Zhi Chen
- Lesia Semenova
- Haiyang Huang
- Chudi Zhong
- Rodrigo C. Barros
- André Carlos Ponce de Leon Ferreira de Carvalho
- Márcio Porto Basgalupp
Citations in the corpus
- Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges (2021, background): “, 2021), (2) stochastic search through the space of trees (e.g., Yang et al., 2017; Gray and Fan, 2008; Papagelis and Kalles, 2000), and (3) customized dynamic programming algorithms that incorporate branch-and-bound techniques for reducing the size of the search space (Hu et al.”
- A Survey of Evolutionary Algorithms for Decision-Tree Induction (2012, background): “This approach or minor variations of it are found in [39], [60], [65], [66], and [74].”