Abstract
A classifier evaluation function based on Bayesian likelihoods of necessity and sufficiency is defined. This function can be used to measure the performance of an arbitrary classifier on a set of examples consisting of labeled positives together with a corpus of unlabeled data. A neural network system has been implemented in which the evaluation function is used as a heuristic to guide search through the space of network weight configurations. Results are presented from testing the system on three artificial datasets. The results are comparable to those that can be obtained using back-propagation, despite the fact that the latter method requires labeled counter-examples.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Quinlan, J.R., C4.5 Programs for Machine Learning, Morgan Kaufmann Publishers, San Francisco, 1993.
Brieman, L., Friedman, J.H., Olshen, R.A. & Stone, C.J., Classification and Regression Trees, Wadsworth International Group, Belmont, California, 1984.
Michalski, R.S., Mozetic, I, Hong, J. and Lavrac, N., The AQ15 Inductive Learning System: An Overview and Experiments, University of Illinois, Urbana-Champaign, 1986.
Rumelhart, D.E. & McClelland, J.L.: Parallel distributed processing: exploration in the microstructure of cognition (Vols. 1 & 2). Cambridge, MA: MIT Press, 1986.
Skabar, A., Biswas, K., Pham, B. and Maeder, A. (1999) “Learning a Concept in the Absence of Labeled Counter-Examples” In Proceedings of the 4 th International Conference on Pattern Recognition and Digital Techniques ICAPRDT’ 99 ISBN-81-7319-247-9, December 27–29, Calcutta, India, pp.32–36.
Holland, J., Adaptation in Natural and Artificial Systems, University of Michigan Press, Ann Arbor, 1983.
Goldberg, D., Genetic algorithms in search, optimization, and machine learning, Addison-Wesley, Reading, Massachusetts, 1989.
De Jong, K.A., Spears, W.M., and Gordon, D.F.: “Using genetic algorithms for concept learning”, Machine Learning, 13, 1993, pp.161–188.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2000 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Skabar, A., Maeder, A., Pham, B. (2000). A Classifier Fitness Measure Based on Bayesian Likelihoods: An Approach to the Problem of Learning from Positives Only. In: Mizoguchi, R., Slaney, J. (eds) PRICAI 2000 Topics in Artificial Intelligence. PRICAI 2000. Lecture Notes in Computer Science(), vol 1886. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44533-1_21
Download citation
DOI: https://doi.org/10.1007/3-540-44533-1_21
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-67925-7
Online ISBN: 978-3-540-44533-3
eBook Packages: Springer Book Archive