Partition Tree Ensembles for Improving Multi-Class Classification
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Summary
This approach achieves significant performance gains over the baseline ToP and AdaBoost methods, across various datasets and loss functions, and outperforms the Random Forest method when the label space exhibits clusters where some classes are more similar to each other than to others.
- Published
- 2025-01-01
- Cited by
- 0
- References
- 33
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:276722354
References
- Kernel Regression Trees
- Applied Regression Analysis and Generalized Linear Models
- Functional Models for Regression Tree Leaves
- Induction of model trees for predicting continuous classes
- A Simple Generalisation of the Area Under the ROC Curve for Multiple Class Classification Problems
- Ensemble selection from libraries of models
- Classification and regression trees
- The clinical interpretation and significance of electronic fetal heart rate patterns 2 h before delivery: an institutional observational study
- AUC Optimization vs. Error Rate Minimization
- A Short Introduction to Boosting
- Ensembles of nested dichotomies for multi-class problems
- Building Ensembles of Adaptive Nested Dichotomies with Random-Pair Selection
- ToPs: Ensemble Learning With Trees of Predictors
- Personalized survival predictions via Trees of Predictors: An application to cardiac transplantation
- Ensembles of evolved nested dichotomies for classification
- Random Forests
- Scikit-learn: Machine Learning in Python
- The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation
- Metrics for Multi-Class Classification: an Overview
- Induction of decision trees
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