Improving classification performance using fuzzy MLP and two-level selective partitioning of the feature space

Abstract

A fuzzy MLP model, developed by one of the authors, is used for obtaining selective two-level partitioning of the feature space in order to improve its classification performance. The model can handle uncertainty and/or impreciseness in the input as well as the output. The input to the network is modelled in terms of linguistic pi-sets whose centres and radii along the feature axes in each partition are generated automatically from the distribution of the training data. The performance of the model at the end of the first stage is used as a criterion for guiding the selection of the appropriate partition to be subdivided at the second stage, in order to improve the effectiveness of the model. A comparative study of the performance of the two-level technique with other methods, viz., the conventional MLP, linear discriminant analysis and the k-nearest neighbours algorithms, is also provided to demonstrate its superiority.