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

In this paper we 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. The comparison showed that ADRS performs better than PCA as a preprocessor to ANN when data mining the datasets of the UCI Machine Learning Repository.

• Type: Conference
• Year: 2008
• Where: ISDA
• Authors: Nicholas Navaroli, David Turner, Arturo I. Concepcion, Robert S. Lynch Jr.
• Keywords: Automatic Data Reduction, Bayesian Probabilistic Classifier, Feature Space, ISDA 2008, Operating Systems