Information gain ratio correction: Improving prediction with more balanced decision tree splits
By Antonin Leroux, Matthieu Boussard, Rémi Dès. 2018
8 citations recorded by the source corpus.
Decision trees algorithms use a gain function to select the best split during the tree's induction. This function is crucial to obtain trees with high predictive accuracy. Some gain functions can suffer from a bias when it compares splits of different arities. Quinlan proposed a gain ratio in C4.5's information gain function to fix this bias. In this paper, we present an updated version of the gain ratio that performs better as it tries to fix the gain ratio's bias for unbalanced trees and some splits with low predictive interest.
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- Aaron M. Roth
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- Manuela M. Veloso
- Peter Stone
- Lucian Busoniu
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- Michael H. Bowling
Citations in the corpus
- Conservative Q-Improvement: Reinforcement Learning for an Interpretable Decision-Tree Policy (2019, background): “The tree, once constructed, also has useful information about which states are treated identically and which features yield the highest information gain [Buntine and Niblett, 1992; Leroux et al. , 2018].”