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Feature selection is an important task in machine learning, which can effectively reduce the dataset dimen-sionality by removing irrelevant and/or redundant features. Although a large body of research deals with feature selection in single-label data, in which measures have been proposed to filter out irrelevant features, this is not the case for(More)
Defining the attributes in terms of fuzzy sets is an essential part in designing a fuzzy system. The main tasks involved in defining the fuzzy data base include deciding the type of fuzzy set (triangular, trapezoidal, etc), the number of fuzzy sets for each attribute, and their distribution in each attribute domain. In the absence of an expert, these(More)
Traditional classification algorithms consider learning problems that contain only one label, i.e., each example is associated with one single nominal target variable characterizing its property. However, the number of practical applications involving data with multiple target variables has increased. To learn from this sort of data, multi-label(More)
The feature selection process aims to select a subset of relevant features to be used in model construction, reducing data dimensionality by removing irrelevant and redundant features. Although effective feature selection methods to support single-label learning are abound, this is not the case for multi-label learning. Furthermore, most of the multi-label(More)
In hierarchical classification tasks using the local approach, an important decision concerns the selection of training examples to build the local classifiers. To this end, several policies, which take into account the class tax-onomy information, have been proposed. However, a study of a comprehensive comparison concerning the performance of these(More)