Classification performance results of various medical diagnostic data sets

Robert S. Lynch Jr.; Peter K. Willett

doi:10.1117/12.475497

16 July 2002 Classification performance results of various medical diagnostic data sets

Proceedings Volume 4733, Component and Systems Diagnostics, Prognostics, and Health Management II; (2002) https://doi.org/10.1117/12.475497
Event: AeroSense 2002, 2002, Orlando, FL, United States

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.

Citation Download Citation

Robert S. Lynch Jr. and Peter K. Willett "Classification performance results of various medical diagnostic data sets", Proc. SPIE 4733, Component and Systems Diagnostics, Prognostics, and Health Management II, (16 July 2002); https://doi.org/10.1117/12.475497

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