Heterogeneous oblique random forest
R Katuwal, PN Suganthan, L Zhang - Pattern Recognition, 2020 - Elsevier
Decision trees in random forests use a single feature in non-leaf nodes to split the data.
Such splitting results in axis-parallel decision boundaries which may fail to exploit the …
Such splitting results in axis-parallel decision boundaries which may fail to exploit the …
Alternating optimization of decision trees, with application to learning sparse oblique trees
MA Carreira-Perpinán… - Advances in neural …, 2018 - proceedings.neurips.cc
Learning a decision tree from data is a difficult optimization problem. The most widespread
algorithm in practice, dating to the 1980s, is based on a greedy growth of the tree structure …
algorithm in practice, dating to the 1980s, is based on a greedy growth of the tree structure …
Efficient non-greedy optimization of decision trees
Decision trees and randomized forests are widely used in computer vision and machine
learning. Standard algorithms for decision tree induction optimize the split functions one …
learning. Standard algorithms for decision tree induction optimize the split functions one …
End-to-end learning of decision trees and forests
Conventional decision trees have a number of favorable properties, including a small
computational footprint, interpretability, and the ability to learn from little training data …
computational footprint, interpretability, and the ability to learn from little training data …
Enhanced oblique decision tree enabled policy extraction for deep reinforcement learning in power system emergency control
Deep reinforcement learning (DRL) algorithms have successfully solved many challenging
problems in various power system control scenarios. However, their decision-making …
problems in various power system control scenarios. However, their decision-making …
Estimation of vegetation indices with Random Kernel Forests
DA Devyatkin - IEEE Access, 2023 - ieeexplore.ieee.org
Vegetation indexes help perform precision farming because they provide useful information
regarding moisture, nutrient content, and crop health. Primary sources of those indexes are …
regarding moisture, nutrient content, and crop health. Primary sources of those indexes are …
Weighted oblique decision trees
BB Yang, SQ Shen, W Gao - Proceedings of the AAAI conference on …, 2019 - aaai.org
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 …
trees include axis-parallel and oblique decision trees; both of them try to find the best splits …
Random kernel forests
DA Devyatkin, OG Grigoriev - IEEE Access, 2022 - ieeexplore.ieee.org
Random forests of axis-parallel decision trees still show competitive accuracy in various
tasks; however, they have drawbacks that limit their applicability. Namely, they perform …
tasks; however, they have drawbacks that limit their applicability. Namely, they perform …
Classification of Pathologies on Medical Images Using the Algorithm of Random Forest of Optimal-Complexity Trees
The authors propose an approach to the construction of classifiers in the class of Random
Forest algorithms. A genetic algorithm is used to determine the optimal combination and …
Forest algorithms. A genetic algorithm is used to determine the optimal combination and …
End-to-end learning of deterministic decision trees
TM Hehn, FA Hamprecht - … , GCPR 2018, Stuttgart, Germany, October 9-12 …, 2019 - Springer
Conventional decision trees have a number of favorable properties, including
interpretability, a small computational footprint and the ability to learn from little training data …
interpretability, a small computational footprint and the ability to learn from little training data …