Uncertain Data Classification using RBF Neural Networks

-Data uncertainty is often found in real-world applications due to reasons such as imprecise measurement, outdated sources, or sampling errors. Numerous factors lead to data uncertainty including data acquisition device error, approximate measurement, sampling fault, transmission latency, data integration error and so on. In this paper, we focus on one commonly encountered type of data uncertainty the exact data value is unavailable and we only know the probability distribution of the data. An intuitive method of handling this type of uncertainty is to represent the uncertain range by its expectation value, and then process it as certain data. This method, although simple and straightforward, may cause valuable information loss. In this paper, we extend the conventional neural networks classifier by applying Radial basis function so that it can take not only certain data but also uncertain probability distribution as the input. RBF’s have very attractive properties such as localization, functional approximation and cluster modelling. These properties made them attractive in many applications.