The Novelty Detection Approach for Different Degrees of Class Imbalance
We show that the novelty detection approach is a viable solution to the class imbalance and examine which approach is suitable for different degrees of imbalance. In experiments using SVM-based classifiers, when the imbalance is extreme, novelty detectors are more accurate than balanced and unbalanced binary classifiers. However, with a relatively moderate imbalance, balanced binary classifiers should be employed. In addition, novelty detectors are more effective when the classes have a non-symmetrical class relationship.
KeywordsSupport Vector Machine Minority Class Class Imbalance Novelty Detector Support Vector Data Description
Unable to display preview. Download preview PDF.
- 1.Kubat, M., Matwin, S.: Addressing the Curse of Imbalanced Training Sets: One-sided Selection. In: Proceedings of 14th International Conference on Machine Learning, pp. 179–186 (1997)Google Scholar
- 3.Elkan, C.: The Foundations of Cost-sensitive Learning. In: Proceedings of the Seventh International Joint Conference on Artificial Intelligence, pp. 973–978 (2001)Google Scholar
- 8.Japkowicz, N.: Concept-Learning in the Absence of Counter-Examples: An Autoassociation-based Approach to Classification. PhD thesis. Rutgers University, New Jersey (1999)Google Scholar
- 14.Schölkopf, B., Platt, J.C., Smola, A.J.: Kernel Method for Percentile Feature Extraction. Technical Report, MSR-TR-2000-22. Microsoft Research, WA (2000)Google Scholar
- 15.Cristianini, N., Shawe-Taylor, J.: An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods. Cambridge University Press, Cambridge (2000)Google Scholar