Weighted Oblique Decision Trees
By Bin-Bin Yang, S. Shen, Wei Gao. 2019
31 citations recorded by the source corpus.
Decision trees have attracted much attention during the past decades. Previous decision trees include axis-parallel and oblique decision trees; both of them try to find the best splits via exhaustive search or heuristic algorithms in each iteration. Oblique decision trees generally simplify tree structure and take better performance, but are always accompanied with higher computation, as well as the initialization with the best axis-parallel splits. This work presents the Weighted Oblique Decision Tree (WODT) based on continuous optimization with random initialization. We consider different weights of each instance for child nodes at all internal nodes, and then obtain a split by optimizing the continuous and differentiable objective function of weighted information entropy. Extensive experiments show the effectiveness of the proposed algorithm.
Subfields: Artificial Intelligence
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In Artificial Intelligence
- Emmanuel Hebrard
- Peter James Stuckey
- C. Leckie
- Emir Demirovi'c
- Jeffrey Chan
- Anna Lukina
- James Bailey
- Kotagiri Ramamohanarao
Citations in the corpus
- MurTree: Optimal Decision Trees via Dynamic Programming and Search (2020, methodology): “Other related work includes heuristic meth-ods for multi-variate trees (Yang et al. (2019)), theoretical analysis of heuristic methods (Blanc et al. (2020)), a fine-grained computational complexity study (Ordyniak and Szei-der (2021)), neural networks for decision trees (Kontschieder et al. (2015);…”