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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...
Ensembles need their base classifiers do not always agree for any prediction (diverse base classifiers). Disturbing Neighbors ( ) is a method for improving the diversity of the base classifiers of any ensemble algorithm. builds for each base classifier a set of extra features based on a 1-Nearest Neighbors (1-NN) output. These 1-NN are built using a small subset of...
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