Accuracy alone can be deceptive when evaluating the performance of a classifier, especially if the problem involves a high number of classes. This paper proposes an approach used for dealing with multi-class problems, which tries to avoid this issue. The approach is based on the Extreme Learning Machine (ELM) classifier, which is trained by using a Differential Evolution (DE) algorithm. Two error measures (Accuracy, $C$, and Sensitivity, S) are combined and applied as a fitness function for the algorithm. The proposed approach is able to obtain multi-class classifiers with a high classification rate level in the global dataset with an acceptable level of accuracy for each class. This methodology is evaluated over seven benchmark classification problems and one real problem, obtaining promising results.
This paper addresses the problem of probability estimation in Multiclass classification tasks combining two well-known data mining techniques: Support Vector Machines and Neural Networks. We present an algorithm which uses both techniques in a two-step procedure. The first step employs Support Vector Machines within a One-vs-All reduction from multiclass to binary approach to obtain the distances between each observation and the Support Vectors representing the classes. The second step uses these distances as inputs for a Neural Network, built with an entropy cost function and softmax transfer function for the output layer where class membership is used for training. Consequently, this network estimates probabilities of class membership for new observations. A benchmark using different databases demonstrates that the proposed algorithm is highly competitive with the most recent techniques for multiclass probability estimation.