Feature space partition: a local-global approach for classification

We propose a local-global classification scheme in which the feature space is, in a first phase, segmented by an unsupervised algorithm allowing, in a second phase, the application of distinct classification methods in each of the generated sub-regions. The proposed segmentation process intentionally produces difficult-to-classify and easy-to-classify sub-regions. Consequently, it is possible to outcome, besides of the classification labels, a measure of confidence for these labels. In almost homogeneous regions, one may be well-nigh sure of the classification result. The algorithm has a built-in stopping criterion to avoid over dividing the space, what would lead to overfitting. The Cauchy-Schwarz divergence is used as a measure of homogeneity in each partition. The proposed algorithm has shown very nice results when compared with 52 prototype selection algorithms. It also brings in the advantage of priory unveiling areas of the feature space where one should expect more (or less) difficult in classifying.