UANN BASED PATTERN CLASSIFIER USING ROUGH SET APPROACH
Data mining currently is a vital and core process because of advances in the technology, and numerous other forms of data. As an essential and significant constituent of data mining, classification has also become vital and a sought-after-option for research. Over the years, as various soft computing tools have been proven to be more complimentary than competitive, many hybrid approaches have evolved for pattern classification. A major thrust has always been there to apply hybrid approaches involving artificial neural network as the main tool for pattern classification. Here, one such hybrid approach is presented with application of Rough set philosophy to unsupervised artificial neural network (UANN) based pattern classifier. This enhances the performance of the classifier and makes it more suitable for real time classification applications dealing with large and unlabeled data. The idea presented here discusses with possibility and application of various Rough set based approaches at different levels of implementation of such classifier. The concluding section presents the results achieved for data of two case studies related to face recognition and mobile handoff prediction.
pattern classification rough sets face recognition handoff unsupervised neural network rough neuron