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Proceedings Paper

Classification performance results of various medical diagnostic data sets
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Paper Abstract

In this paper, the Bayesian Data Reduction Algorithm is applied to a collection of medical diagnostic data sets 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 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 is shown to outperform the linear classifier by obtaining a lower probability of error, as averaged over all data sets.

Paper Details

Date Published: 16 July 2002
PDF: 8 pages
Proc. SPIE 4733, Component and Systems Diagnostics, Prognostics, and Health Management II, (16 July 2002); doi: 10.1117/12.475497
Show Author Affiliations
Robert S. Lynch Jr., Naval Undersea Warfare Ctr. (United States)
Peter K. Willett, Univ. of Connecticut (United States)

Published in SPIE Proceedings Vol. 4733:
Component and Systems Diagnostics, Prognostics, and Health Management II
Peter K. Willett; Thiagalingam Kirubarajan, Editor(s)

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