Text classification using the σ-FLNMAP neural network

A novel neural network, namely sigma Fuzzy Lattice Neural network with MAPping or sigma-FLNMAP for short, is presented and applied to classification of text (documents) from the Brown Corpus benchmark collection of documents. The sigma-FLNMAP is presented here as an enhanced extension of the fuzzy-ARTMAP neural network in the framework of fuzzy lattices. An individual sigma-FLNMAP's classification accuracy is improved by training an ensemble of sigma-FLNMAP modules on different permutations of the training data. Several different vector representations of a document are employed. The results, in a series of experiments, compare favorably with the results by other classification algorithms including K-Nearest Neighbor and Naive Bayes Classifiers.