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.
A component selection is a crucial problem in Component-Based Software Engineering (CBSE), which is concerned with the assembly of pre-existing software components.
We are approaching the component selection involving dependencies between components. We formulate the problem as multiobjective, involving two objectives and one constraint. The approach used is an evolutionary computation technique. The experiments and comparisons with the greedy approach show the effectiveness of the proposed approach.