Abstract
In this paper we aim to investigate the trade off in selection of an accurate, robust and cost-effective classification model for binary classification problem. With empirical observation we present the evaluation of one-class and two-class classification model. We have experimented with four two-class and one-class classifier models on five UCI datasets. We have evaluated the classification models with Receiver Operating Curve (ROC), Cross validation Error and pair-wise measure Q statistics. Our finding is that in the presence of large amount of relevant training data the two-class classifiers perform better than one-class classifiers for binary classification problem. It is due to the ability of the two class classifier to use negative data samples in its decision. In scenarios when sufficient training data is not available the one-class classification model performs better.
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
Nisenson, M., Yariv, I., El-Yaniv, R., Meir, R.: Towards behaviometric security systems: Learning to identify a typist. In: Lavrač, N., Gamberger, D., Todorovski, L., Blockeel, H. (eds.) PKDD 2003. LNCS (LNAI), vol. 2838, pp. 363–374. Springer, Heidelberg (2003)
Kuncheva, L.I., Whitaker, C.J.: Measures of diversity in classifier ensembles and their relationship with the ensemble accuracy. Machine Learning 51, 181–207 (2003)
Yu, E., Cho, S.: Novelty detection approach for keystroke dynamics identity verification. In: Proceedings of the 4th International Conference on Intelligent Data Engineering and Automated Learning, Berlin, Germany, pp. 1016–1023. Springer, Heidelberg (2003)
Koppel, M., Schler, J.: Authorship verification as a one-class classification problem. In: Proceedings of the 21st International Conference on Machine Learning, pp. 489–495. ACM Press, New York (2004)
Bishop, C.: Neural Networks for Pattern Recognition. Oxford University Press, Oxford (1995)
Pattern Recognition Toolbox (PRTOOLS) for Matlab implemented by R.W.P.Duin
One Class classification toolbox (ddtools) for Matlab implemented by D.M.J Tax
Tax, D.M.J., Duin, R.W.P.: Support Vector Domain Descriptor. Pattern Recognition Letters 20(11-12), 1191–1199 (1999)
Duda, R.O., Hart, P.E., Stork, D.G.: Pattern classification, 2nd edn. John Wiley and Sons, New York (2001)
Webb, Statistical Pattern Recognition. John Wiley & Sons, New York (2002)
Liu, C., Wechsler, H.: Robust Coding Schemes for Indexing and Retrieval from Large Face Databases. IEEE Transactions on Image Processing 9(1), 132–136 (2000)
Tax, D.M.J., Duin, R.P.W.: Combining One-Class Classifiers. In: Kittler, J., Roli, F. (eds.) MCS 2001. LNCS, vol. 2096, p. 299. Springer, Heidelberg (2001)
Tax, D.M.J.: One-Class Classification, Concept Learning in the Absence of Counter Examples. Ph.D. Thesis, Delft University of Technology, Delft, Netherland (2001)
Tax, D.M.J., Duin, R.P.W.: Support Vector Data Description. Machine Learning 54(1), 45–66 (2004)
Kuncheva, L.I.: Combining Pattern Classifiers: Methods and Algorithms. Wiley, Chichester (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Khan, M.A., Jan, Z., Ishtiaq, M., Khan, M.A., Mirza, A.M. (2009). Selection of Accurate and Robust Classification Model for Binary Classification Problems. In: Ślęzak, D., Pal, S.K., Kang, BH., Gu, J., Kuroda, H., Kim, Th. (eds) Signal Processing, Image Processing and Pattern Recognition. SIP 2009. Communications in Computer and Information Science, vol 61. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10546-3_20
Download citation
DOI: https://doi.org/10.1007/978-3-642-10546-3_20
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-10545-6
Online ISBN: 978-3-642-10546-3
eBook Packages: Computer ScienceComputer Science (R0)