Performance analysis of the Bayesian data reduction algorithm

Douglas M. Kline and Craig S. Galbraith

International Journal of Data Mining, Modelling and Management, 2009, vol. 1, issue 3, 223-236

Abstract: This paper compares the performance of the recently proposed Bayesian data reduction algorithm (BDRA) with a rigorously trained automated feed-forward back-propagation artificial neural network (ANN) classifier on a number of benchmark problems. Using the UCI Machine Learning Repository, six two-group classification problems were examined: Wisconsin breast cancer disease, glass identification, ionosphere, IRIS plant, Pima Indian diabetes and liver disorders. Using re-sampling process to reduce sample bias, the two classifiers were compared along the dimensions of in-sample classification, test-sample classification, dimensionality reduction and training time requirements. Significant differences between performances were determined by pair-wise repeated measures t-tests between means. The results indicated that the BDRA consistently outperformed the neural network in dimensionality reduction and training time requirements, while obtaining, with the exception of one database, comparable classification rates. For benchmarking purposes, both the BDRA and ANN were compared with a step-wise linear regression classification model.

Keywords: artificial neural networks; ANNs; classification; Bayesian rules; data reduction; machine learning; dimensionality reduction; training time. (search for similar items in EconPapers)
Date: 2009
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