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Robust neural learning from unbalanced data samples

This paper describes the result of our study on neural learning to solve the classification problem in which the data is unbalanced and noisy. Our study was conducted on three different neural network architectures, multilayered back propagation, radial basis function, and fuzzy ARTMAP with training methods including duplicating minority class samples and the Snowball technique. Three major issues are addressed: neural learning from unbalanced data samples, neural learning from noise data, and making intentional biased decisions. The application considered in this study is classifying good(pass)/bad(fail) vehicles. Experiments are conducted on data samples downloaded directly from test sites of automobile assembly.