Abstract
Weighted relative accuracy was proposed in [4] as an alternative to classification accuracy typically used in inductive rule learners. Weighted relative accuracy takes into account the improvement of the accuracy relative to the default rule (i.e., the rule stating that the same class should be assigned to all examples), and also explicitly incorporates the generality of a rule (i.e., the number of examples covered). In order to measure the predictive performance of weighted relative accuracy, we implemented it in the rule induction algorithm CN2. Our main results are that weighted relative accuracy dramatically reduces the size of the rule sets induced with CN2 (on average by a factor 9 on the 23 datasets we used), at the expense of only a small average drop in classification accuracy.
Chapter PDF
Similar content being viewed by others
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Clark, P. and Boswell, R. (1991) Rule induction with CN2: Some recent improvements. In Proceedings of the Fifth European Working Session on Learning: 151–163. Springer-Werlag.
Clark, P. and Niblett, T. (1989) The CN2 induction algorithm. Machine Learning Journal, 3(4): 261–283.
Džeroski, S., Cestnik, B. and Petrovski, I. (1993) Using the m-estimate in rule induction. Journal of Computing and Information Technology, 1(1):37–46.
Lavrač, N., Flach, P. and Zupan, B. (1999) Rule Evaluation Measures: A Unifying View. In Proceedings of the Ninth International Workshop on Inductive Logic Programming, volume 1634 of Lecture Notes in Artificial Intelligence: 74–185. Springer-Verlag.
Muggleton, S., Srinivasan, A., King R. and Sternberg, M. (1998) Biochemical knowledge discovery using Inductive Logic Programming. In Motoda, H. (editor)Proceedings of the first Conference on Discovery Science. Springer-Verlag.
Murphy, P. M. and Aha, D. W. (1994) UCI repository of machine learning databases [http://www.ics.uci.edu/~mlearn/MLRepository.html]. Irvine, CA: University of California, Department of Information and Computer Science.
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
Todorovski, L., Flach, P., Lavrač, N. (2000). Predictive Performance of Weighted Relative Accuracy. In: Zighed, D.A., Komorowski, J., Żytkow, J. (eds) Principles of Data Mining and Knowledge Discovery. PKDD 2000. Lecture Notes in Computer Science(), vol 1910. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45372-5_25
Download citation
DOI: https://doi.org/10.1007/3-540-45372-5_25
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-41066-9
Online ISBN: 978-3-540-45372-7
eBook Packages: Springer Book Archive