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17 March 2008 Performance comparison of the automatic data reduction system (ADRS)
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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 called the Automatic Data Reduction System (ADRS). The primary contribution of this work will be to demonstrate and compare different approaches to the feature search (e.g., forward versus backward searching), and show how performance is impacted for each data set. Additionally, the performance of the ADRS with the UCI data will be compared to an Artificial Neural Network (ANN). In this case, results are shown for the ANN both with and without the utilization of Principal Components Analysis (PCA) to reduce the dimension of the feature data. Overall, it is shown that the BDRA's performance with the UCI data is superior to that of the ANN.
© (2008) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Dan Patterson, David Turner, Arturo Concepcion, and Robert Lynch "Performance comparison of the automatic data reduction system (ADRS)", Proc. SPIE 6973, Data Mining, Intrusion Detection, Information Assurance, and Data Networks Security 2008, 69730H (17 March 2008);

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