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Measuring the quality of a contrast pattern is an active and relevant area of pattern recognition and data mining. Quality measures are important tools in very different scenarios like supervised classification, pattern based clustering, and association rule mining. Consequently, and due to the large collection of available measures, it is important to perform comparative studies for each particular context. Most published studies comparing quality measures are theoretical and in the context of association rule evaluation. In this paper, we present an empirical comparison of the behavior of 33 quality measures in the context of supervised classification and contrast pattern filtering. A comprehensive experimentation using several databases compares the behavior of these measures in three different contexts: as aggregation value, as pattern evaluation for classification, and as pattern evaluation for filtering. Experiments also show that top-accurate quality measures for classification have a deceptive performance for pattern filtering, because they cannot distinguish among patterns with zero support in the negative class. [ABSTRACT FROM AUTHOR]