Regional Classifiers: A Novel Framework for Pattern Classification

The global classification paradigm uses the entire training set for building a single discriminating model. Alternatively, the local classification approach builds multiple discriminating models using smaller subsets of the training data. By considering these two paradigms as the extremes of a spectrum of possibilities, in this paper we introduce a novel two-stage framework for building classification models based on the clustering of the self-organizing map (SOM) approach. ata samples are submitted to the SOM as a preprocessing stage. Then, clustering algorithms (e.g.K-means) are applied to the prototype vectors of the SOM aiming at organizing them in well-defined groups. By applying this two-stage strategy, we show how to build accurate classifying models, henceforth referred to as regional classifiers, using the subset of samples mapped to a specific cluster of SOM prototypes. A comparative study is carried out in order to evaluate the effectiveness of the proposed approach on several data sets.