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Many real-life problems can be described as unbalanced, where the number of instances belonging to one of the classes is much larger than the numbers in other classes. Examples are spam detection, credit card fraud detection or medical diagnosis. Ensembles of classifiers have acquired popularity in this kind of problems for their ability to obtain better results than individual classifiers. The most...
This paper presents an experimental study using different projection strategies and techniques to improve the performance of Support Vector Machine (SVM) ensembles. The study has been made over 62 UCI datasets using Principal Component Analysis (PCA) and three types of Random Projections (RP), taking into account the size of the projected space and using linear SVMs as base classifiers. Random Projections...
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