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Functional Trees are one type of multivariate trees. This work studies the performance of different ensemble methods (Bagging, Random Subspaces, AdaBoost, Rotation Forest) using three variants (multivariate internal nodes, multivariate leaves or both) of these trees as base classifiers. The best results, for all the ensemble methods, are obtained using Functional Trees with multivariate leaves and...
Ensembles of decision trees are considered for imbalanced datasets. Conventional decision trees (C4.5) and trees for imbalanced data (CCPDT: Class Confidence Proportion Decision Tree) are used as base classifiers. Ensemble methods, based on undersampling and oversampling, for imbalanced data are considered. Conventional ensemble methods, not specific for imbalanced data, are also studied: Bagging,...
In pattern recognition many methods need numbers as inputs. Using nominal datasets with these methods requires to transform such data into numerical. Usually, this transformation consists in encoding nominal attributes into a group of binary attributes (one for each possible nominal value). This approach, however, can be enhanced for certain methods (e.g., those requiring linear separable data representations)...
Model trees are decision trees with linear regression functions at the leaves. Although originally proposed for regression, they have also been applied successfully in classification problems. This paper studies their performance for imbalanced problems. These trees give better results that standard decision trees (J48, based on C4.5) and decision trees specific for imbalanced data (CCPDT: Class Confidence...
This paper proposes a method for constructing ensembles of decision trees: GRASP Forest. This method uses the metaheuristic GRASP, usually used in optimization problems, to increase the diversity of the ensemble. While Random Forest increases the diversity by randomly choosing a subset of attributes in each tree node, GRASP Forest takes into account all the attributes, the source of randomness in...
Disturbing Neighbors (DN) is a method for generating classifier ensembles. Moreover, it can be combined with any other ensemble method, generally improving the results. This paper considers the application of these ensembles to imbalanced data: classification problems where the class proportions are significantly different. DN ensembles are compared and combined with Bagging, using three tree methods...
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