An incremental method for finding multivariate splits for decision trees
Proceedings of the seventh international conference (1990) on Machine learning
Neural networks and the bias/variance dilemma
Neural Computation
Machine Learning
Machine Learning
A Comparative Analysis of Methods for Pruning Decision Trees
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Random Subspace Method for Constructing Decision Forests
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine Learning
Dynamic Automatic Model Selection
Dynamic Automatic Model Selection
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
A system for induction of oblique decision trees
Journal of Artificial Intelligence Research
Constructing diverse classifier ensembles using artificial training examples
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Neural networks for classification: a survey
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Top-down induction of decision trees classifiers - a survey
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Ensembling local learners ThroughMultimodal perturbation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Neural Networks
Perceptron-based learning algorithms
IEEE Transactions on Neural Networks
Classifiability-based omnivariate decision trees
IEEE Transactions on Neural Networks
Classification trees with neural network feature extraction
IEEE Transactions on Neural Networks
An improved algorithm for neural network classification of imbalanced training sets
IEEE Transactions on Neural Networks
Expert Systems with Applications: An International Journal
Computational Statistics & Data Analysis
Model selection in omnivariate decision trees using Structural Risk Minimization
Information Sciences: an International Journal
Decision trees: a recent overview
Artificial Intelligence Review
Building fast decision trees from large training sets
Intelligent Data Analysis
Hi-index | 0.03 |
Decision trees recursively partition the instance space by generating nodes that implement a decision function belonging to an a priori specified model class. Each decision may be univariate, linear or nonlinear. Alternatively, in omnivariate decision trees, one of the model types is dynamically selected by taking into account the complexity of the problem defined by the samples reaching that node. The selection is based on statistical tests where the most appropriate model type is selected as the one providing significantly better accuracy than others. In this study, we propose the use of model ensemble-based nodes where a multitude of models are considered for making decisions at each node. The ensemble members are generated by perturbing the model parameters and input attributes. Experiments conducted on several datasets and three model types indicate that the proposed approach achieves better classification accuracies compared to individual nodes, even in cases when only one model class is used in generating ensemble members.