  The use of alternative measures to evaluate classifier performance is gaining
attention, specially for imbalanced problems. However, the use of these
measures in the classifier design process is still unsolved. In this work we
propose a classifier designed specifically to optimize one of these alternative
measures, namely, the so-called F-measure. Nevertheless, the technique is
general, and it can be used to optimize other evaluation measures. An algorithm
to train the novel classifier is proposed, and the numerical scheme is tested
with several databases, showing the optimality and robustness of the presented
classifier.
