The evaluation of classifiers is not an easy task. There are various ways of testing them and measures to estimate their performance. The great majority of these measures were defined for two-class problems and there is not a consensus about how to generalize them to multiclass problems. This paper proposes the extension of the F-measure and G-mean in the same fashion as carried out with the AUC. Some datasets with diverse characteristics are used to generate fuzzy classifiers and C4.5 trees. The most common evaluation metrics are implemented and they are compared in terms of their output values: the greater the response the more optimistic the measure. The results suggest that there are two well-behaved measures in opposite roles: one is always optimistic and the other always pessimistic.

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This paper investigates the use of three external feedback connections in the development of new recurrent fuzzy models for prediction of nonlinear dynamic systems. These models are formulated by state space equations. The state transition function is a TSK recurrent fuzzy system with an external feedback connection and adaptive delayed operators, and the output function is a polynomial function of the states. The identification of the model's parameters is carried out by a canonical differential evolution algorithm. The model performance was evaluated in benchmark problems found in the literature and the results demonstrated that the aiding of external feedback enhanced the recurrent fuzzy system quality, yielding models with good performance.

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