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Proceedings Paper

Performance comparison of the automatic data reduction system (ADRS)
Author(s): Dan Patterson; David Turner; Arturo Concepcion; Robert Lynch
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Paper Abstract

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 http://wiki.csci.csusb.edu/bdra/Main_Page) 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.

Paper Details

Date Published: 17 March 2008
PDF: 8 pages
Proc. SPIE 6973, Data Mining, Intrusion Detection, Information Assurance, and Data Networks Security 2008, 69730H (17 March 2008); doi: 10.1117/12.777903
Show Author Affiliations
Dan Patterson, California State Univ., San Bernardino (United States)
David Turner, California State Univ., San Bernardino (United States)
Arturo Concepcion, California State Univ., San Bernardino (United States)
Robert Lynch, Naval Undersea Warfare Ctr. (United States)


Published in SPIE Proceedings Vol. 6973:
Data Mining, Intrusion Detection, Information Assurance, and Data Networks Security 2008
William J. Tolone; William Ribarsky, Editor(s)

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