Differential Evolution Classifier in Noisy Settings and with Interacting Variables

In this paper, we have studied the performance of a differential evolution (DE) classifier in classifying data in noisy settings. We have also studied the performance in handling extra variables which simply consists of gaussian noise. Furthermore, we have carried out the classification by adding on all two component interaction terms as extra variables into the data. Also, in this situation it is crucial to have a classifier which is tolerant to noisy variables. Namely, even though there can be interaction effects in the data that can influence classification results positively, it is usually not known a priori which particular interaction components are contributing to the classification results. Therefore, we need to add all possible combinations despite the likelihood of then creating also some noisy variables which do not influence the classification accuracy, or which actually reduce the accuracy. In experimentation, we used four widely applied test data sets; the new-thyroid, heart-statlog, Hungarian heart and lenses data sets. The results indicated the DE classifier to be robust from the noise tolerance point of view in all studied cases and situations. The results suggest that the DE classifier is useful especially in the cases where interaction effects may have a significant influence to the classification accuracy.