Maria Teresa Ricamato

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Two class classification problems in real world are often characterized by imbalanced classes. This is a serious issue since a classifier trained on such a data distribution typically exhibits a prediction accuracy highly skewed towards the majority class. To improve the quality of the classifier, many approaches have been proposed till now for building(More)
The class imbalance is a critical problem in classification tasks related to many real world applications. A large number of solutions were proposed in literature, both at the algorithmic and data levels. In this paper we analyze the second kind of approach and, in particular, we focus our attention on the use of Multiple Classification Systems where each(More)
The combination of classifiers is an established technique to improve the classification performance. When dealing with two-class classification problems, a frequently used performance measure is the Area under the ROC curve (AUC) since it is more effective than accuracy. However, in many applications, like medical or biometric ones, tests with false(More)
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