Performance Comparison of ADRS and PCA as a Preprocessor to ANN for Data Mining

In this paper, the authors compared the performance of the Automatic Data Reduction System (ADRS) and Principal Component Analysis (PCA) as a preprocessor to Artificial Neural Networks (ANN). ADRS is based on a Bayesian probabilistic classifier that is used with a quantization process that results in a simplification of the feature space, including elimination of irrelevant features. ADRS has the advantage of retaining the original names of the features even though the feature space has been modified. Thus, results are easier to interpret than those of PCA and ANN, which transform the feature space in a way that obscures the original meanings of the features.