Quantizing features independently in the Bayesian data reduction algorithm

In this paper, the Bayesian data reduction algorithm (BDRA) is modified to quantize each feature (i.e., those that are continuous valued and not naturally discrete, or categorical), independently, allowing a best set of thresholds to be found for all dimensions contained in the data. The algorithm works by initially quantizing each feature with at least ten discrete levels (via percentiles), where the BDRA then proceeds to reduce this to a number of levels yielding best performance. The BDRA then trains on all independently quantized features simultaneously, finding the best overall quantization complexity of the data that minimizes the training error. Results are demonstrated illustrating the performance of the new modified algorithm for various data sets found at the University of California at Irvine's (UCI) repository of machine learning databases.