Machine Learning
The Random Subspace Method for Constructing Decision Forests
IEEE Transactions on Pattern Analysis and Machine Intelligence
A simple, fast, and effective rule learner
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Using Model Trees for Classification
Machine Learning
MultiBoosting: A Technique for Combining Boosting and Wagging
Machine Learning
Machine Learning
SMO algorithm for least-squares SVM formulations
Neural Computation
Multiclass Alternating Decision Trees
ECML '02 Proceedings of the 13th European Conference on Machine Learning
Stacking Bagged and Dagged Models
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Inference for the Generalization Error
Machine Learning
Top-Down Induction of Model Trees with Regression and Splitting Nodes
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Dynamics of AdaBoost: Cyclic Behavior and Convergence of Margins
The Journal of Machine Learning Research
Machine Learning
Rotation Forest: A New Classifier Ensemble Method
IEEE Transactions on Pattern Analysis and Machine Intelligence
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
A composite logistic regression approach for ordinal panel data regression
International Journal of Data Analysis Techniques and Strategies
Evidence and scenario sensitivities in naive Bayesian classifiers
International Journal of Approximate Reasoning
Statistical Instance-Based Pruning in Ensembles of Independent Classifiers
IEEE Transactions on Pattern Analysis and Machine Intelligence
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
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Structurally, a model tree is a regression method that takes the form of a decision tree with linear regression functions instead of terminal class values at its leaves. In this study, model trees were coupled with a rotation-based ensemble for solving classification problems. In order to apply this regression technique to classification problems, we considered the conditional class probability function and sought a model-tree approximation to it. During classification, the class whose model tree generated the greatest approximated probability value was chosen as the predicted class. We performed a comparison with other well-known ensembles of decision trees on standard benchmark data sets, and the performance of the proposed technique was greater in most cases.