Robert S. Lynch

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In this paper, a method of classification referred to as the Bayesian data reduction algorithm (BDRA) is developed. The algorithm is based on the assumption that the discrete symbol probabilities of each class are a priori uniformly Dirichlet distributed, and it employs a "greedy" approach (which is similar to a backward sequential feature search) for(More)
In this paper, real data sets from the UCI Repository are mined and quantized to reduce the dimensionality of the feature space for best classification performance. The approach utilized to mine the data is based on the Bayesian Data Reduction Algorithm (BDRA), which has been recently developed into a windows based system by California State University (see(More)
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(More)