Abstract
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.
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
Demsar, J.: Statistical comparisons of classifiers over multiple data sets. Journal of Machine Learning Research 7, 1–30 (2006)
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)
García, S., Fernández, A., Luengo, J., Herrera, F.: Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: Experimental analysis of power. Inf. Sci. 180(10), 2044–2064 (2010)
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)
Krawczyk, B., Woźniak, M.: Diversity measures for one-class classifier ensembles. Neurocomputing 126, 36–44 (2014)
Lin, C., Chen, W., Qiu, C., Wu, Y., Krishnan, S., Zou, Q.: Libd3c: Ensemble classifiers with a clustering and dynamic selection strategy. Neurocomputing 123, 424–435 (2014)
Łysiak, R., Kurzyński, M., Wołoszynski, T.: Optimal selection of ensemble classifiers using measures of competence and diversity of base classifiers. Neurocomputing 126, 29–35 (2014)
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)
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)
Woloszynski, T., Kurzynski, M.: On a New Measure of Classifier Competence Applied to the Design of Multiclassifier Systems. In: Foggia, P., Sansone, C., Vento, M. (eds.) ICIAP 2009. LNCS, vol. 5716, pp. 995–1004. Springer, Heidelberg (2009)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Krawczyk, B., Woźniak, M. (2014). One-Class Classification Ensemble with Dynamic Classifier Selection. In: Zeng, Z., Li, Y., King, I. (eds) Advances in Neural Networks – ISNN 2014. ISNN 2014. Lecture Notes in Computer Science(), vol 8866. Springer, Cham. https://doi.org/10.1007/978-3-319-12436-0_60
Download citation
DOI: https://doi.org/10.1007/978-3-319-12436-0_60
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-12435-3
Online ISBN: 978-3-319-12436-0
eBook Packages: Computer ScienceComputer Science (R0)