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Proceedings Paper

Comparative study of public-domain supervised machine-learning accuracy on the UCI database
Author(s): Peter W. Eklund
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Paper Abstract

This paper surveys public domain supervised learning algorithms and performs accuracy (error rate) analysis of their classification performance on unseen instances for twenty-nine of the University of California at Irvine machine learning datasets. The learning algorithms represent three types of classifiers: decision trees, neural networks and rule-based classifiers. The study performs data analysis and examines the effect of irrelevant attributes to explain the performance characteristics of the learning algorithms. The survey concludes with some general recommendations about the selection of public domain machine-learning algorithms relative to the properties of the data examined.

Paper Details

Date Published: 25 February 1999
PDF: 12 pages
Proc. SPIE 3695, Data Mining and Knowledge Discovery: Theory, Tools, and Technology, (25 February 1999); doi: 10.1117/12.339989
Show Author Affiliations
Peter W. Eklund, Griffith Univ. (Australia)


Published in SPIE Proceedings Vol. 3695:
Data Mining and Knowledge Discovery: Theory, Tools, and Technology
Belur V. Dasarathy, Editor(s)

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