Classification performance of various real-life data sets when the features are discretized

The Bayesian data reduction algorithm is applied to a collection of thirty real-life data sets primarily found at the University of California at Irvine´s Repository of Machine Learning databases. The algorithm works by finding the best performing quantization complexity of the feature vectors, and this makes it necessary to discretize all continuous valued features. Therefore, results are given by showing the initial quantization of the continuous valued features that yields best performance. Further, the Bayesian data reduction algorithm is also compared to a conventional linear classifier, which does not discretize any feature values. In general, the Bayesian data reduction algorithm outperforms the linear classifier by obtaining a lower probability of error, as averaged over all thirty data sets