Feature space transformations and model selection to improve the performance of classifiers

Improving the performance of classifiers is the realm of prototype selection and kernel transformations. Prototype selection has been used to reduce the space complexity of k-Nearest Neighbors classifiers and to improve its accuracy, and kernel transformations enhanced the performance of linear classifiers by converting a non-linear separable problem into a linear one in the transformed space. Our proposal combines, in a model selection scheme, these transformations with classic algorithms such as Na\"ive Bayes and k-Nearest Neighbors to produce a competitive classifier. We analyzed our approach on different classification problems and compared it to state-of-the-art classifiers. The results show that the methodology proposed is competitive, obtaining the lowest rank among the classifiers being compared.