Feature

Abstract. Feature selection is a process followed in order to improve the generalization and the performance of several classification and/or regression algorithms. Feature selection processes are divided in two categories, the filter and the wrapper approach. The formal is performed independently of the learning algorithm while the later makes use of the algorithm in an iterative way. As [1] describe, the feature weighting algorithms are divided into two categories: the filtering methods and the wrapper methods. The former is a no-feedback, pre-selection approach where the selection of the feature subset is performed independently of the learning algorithm. The later is an iterative method that encapsulates the learning algorithm in the feature selection process.