Soft computing is popularly referred to as a collection of methodologies that work synergistically and provide flexible information processing capabilities for handling real-life situations. Its aim is to exploit the tolerance for imprecision, uncertainty and approximate reasoning in order to achieve tractability and robustness. Currently, fuzzy logic, artificial neural networks, and genetic algorithms are three main components of soft computing. In this chapter, we show the application of soft computing techniques to solve high dimensional problems. We have taken a multi-class classification problem of bond rating prediction with 45 input variables and have used soft computing techniques to solve it. Two techniques, namely dimensionality reduction technique and variable reduction techniques, have been tried and their performances are compared. Hybrid networks are found to give better results compared to normal fuzzy and ANN methods. We also have compared all the results with normal regression techniques.
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