Jesús González

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We introduce a method to deal with the problem of learning from imbalanced data sets, where examples of one class significantly outnumber examples of other classes. Our method selects minority examples from misclassified data given by an ensemble of classifiers. Then, these instances are over-sampled to create new synthetic examples using a variant of the(More)
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