Rough Set Approach for Overall Performance Improvement of an Unsupervised ANN-Based Pattern Classifier

Most conventional approaches to pattern classification using unsupervised ANN use clusterification with the entire feature set. The redundancy (dependence) of some features in such cases makes feature space dimensionality too complex to handle. Early convergence is another factor desired for the training phase in networks trying different neural architectures or learning algorithms. As approaches evolve and are applied, the hybridization of neural concepts with other tools has yielded useful results. A rough set is one such approximation tool that works well when in environments heavy with inconsistency and ambiguity in data or involving missing data. Approaches using rough sets may be used at the preprocessing, learning and neuron architectural levels. Preprocessing and architectural approaches are discussed here using Rough sets to improve overall performance of pattern classifiers used in character recognitions.