One-Class Classification Ensemble with Dynamic Classifier Selection
The main problem of one-class classification lies in selecting the model for the data, as we do not have any access to counterexamples, and cannot use standard methods for estimating the quality of the classifier. Therefore ensemble methods that can utilize more than one model, are a highly attractive solution which prevents the situation of choosing the weakest model and improves the robustness of our recognition system. However, one cannot assume that all classifiers available in the pool are in general accurate - they may have some local areas of competence in which they should be utilized. We present a dynamic classifier selection method for constructing efficient one-class ensembles. We propose to calculate the individual classifier competence in a given validation point and use them to estimate competence of each classifier over the entire decision space with a Gaussian potential function. Experimental analysis, carried on a number of benchmark data and backed-up with a thorough statistical analysis prove its usefulness.
KeywordsOne-class classification Classifier selection Competence measure
Unable to display preview. Download preview PDF.
- 2.Galar, M., Fernández, A., Barrenechea Tartas, E., Bustince Sola, H., Herrera, F.: Dynamic classifier selection for one-vs-one strategy: Avoiding non-competent classifiers. Pattern Recognition 46(12), 3412–3424 (2013)Google Scholar
- 4.Hung-Ren Ko, A., Sabourin, R., de Souza Britto Jr., A.: From dynamic classifier selection to dynamic ensemble selection. Pattern Recognition 41(5), 1718–1731 (2008)Google Scholar
- 8.Tax, D.M.J., Müller, K.: A consistency-based model selection for one-class classification. In: Proceedings of the International Conference on Pattern Recognition, vol. 3, pp. 363–366 (2004) (cited by since 1996:12)Google Scholar
- 9.Tax, D.M.J., Duin, R. P. W.: Characterizing one-class datasets. In: Proceedings of the Sixteenth Annual Symposium of the Pattern Recognition Association of South Africa, pp. 21–26 (2005)Google Scholar