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In most traditional classification methods, each instance is associated with one single nominal target variable (single-label problems). However, there are also cases where an instance can be associated with more than one label simultaneously, referring to as multi-label classification problems. One of the main problems with classification methods is that many of these require a high number of instances...
One of the most important steps in the design of a multi-classifier system (MCS), also known as ensemble, is the choice of the components (classifiers). This step is very important to the overall performance of a MCS since the combination of a set of identical classifiers will not outperform the individual members. The ideal situation would be a set of classifiers with uncorrelated errors – they would...
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