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Feature selection has been used widely for a variety of data, yielding higher speeds and reduced computational cost for the classification process. However, it is in microarray datasets where its advantages become more evident and are more required. In this paper we present a novel approach to accomplish this based on the concept of discernibility that we introduce to depict how separated the classes...
Reducing the dimensionality of a dataset is an important and often challenging task. This can be done by either reducing the number of features, a task called feature selection, or by reducing the number of patterns, called data reduction. In this paper we propose methods that employ a novel concept called Discernibility for achieving these two tasks separately, with the aim to solve classification...
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