Unsupervised Neural Network Based Pattern Classifiers with Rough Set Approach
Ashwin Kothari, Avinash G. Keskar, Sio-Iong Ao · AIP conference proceedings · 2009
Early Convergence, input feature space with minimal dimensions and good classification accuracy are always the most desired characteristics of an unsupervised neural network based pattern classifier. To achieve these, various approaches comprising of various soft computing tools can be used at different levels of implementations of such classifiers. Rough set is also one such tool, which can be used at either preprocessing level or learning level or architectural implementation level. Approaches using rough sets at first and the third levels are discussed here. Use of rough sets at the above stated levels result in dimensionality reduction of feature space through preprocessing and early convergence through rough neuron based neural network.