Abstract
In this paper, a Single Classifier-based Multiple Classification Scheme (SMCS) is proposed as an alternative multiple classification scheme. The SMCS uses only a single classifier to generate multiple classifications for a given test data point. Because of the presence of multiple classifications, classification combination schemes, such as majority voting, can be applied, and so the mechanism may improve the recognition rate in a manner similar to that of Multiple Classifier Systems (MCS). The experimental results confirm the validity of the proposed SMCS as applicable to many classification systems.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Bottou, L., Vapnik, V.N.: Local Learning Algorithms. Neural Computation 4(6), 888–900 (1992)
Decoste, D., Schoelkopf, B.: Training invariant support vector machines. Machine Learning 46(1), 161–190 (2002)
Duin, R.P., Juszczak, P., Paclik, P., Pekalska, E., de Ridder, D., Tax, D., Verzakov, S.: PR-Tools4.1, a matlab toolbox for pattern recognition (2007), http://prtools.org
Ko, A.H.R., Sabourin, R., de Souza Britto Jr., A.: Combining diversity and classification accuracy for ensemble selection in random subspaces. In: Proceedings of the International Joint Conference on Neural Networks, pp. 2144–2151 (2006)
Ko, A.H.R., Sabourin, R., de Souza Britto Jr., A.: Compound Diversity Functions for Ensemble Selection. International Journal of Pattern Recognition and Artificial Intelligence 23(4), 659–686 (2009)
Ko, A.H.R., Cavalin, P., Sabourin, R., de Souza Britto Jr., A.: Leave-One-Out-Training and Leave-One-Out-Testing Hidden Markov Models for a Handwritten Numeral Recognizer: the Implication of a Single Classifier and Multiple Classifications. IEEE Transactions on Pattern Analysis and Machine Intelligence 31(12), 2168–2178 (2009)
Ko, A.H.R., Sabourin, R., de Souza Britto Jr., A.: From dynamic classifier selection to dynamic ensemble selection. Pattern Recognition 41(5), 1718–1731 (2008)
Kuncheva, L.I., Skurichina, M., Duin, R.P.W.: An Experimental Study on Diversity for Bagging and Boosting with Linear Classifiers. International Journal of Information Fusion 3(2), 245–258 (2002)
Kuncheva, L.I., Rodriguez, J.J.: Classifier ensembles with a random linear oracle. IEEE Transactions on Knowledge and Data Engineering 19(4), 500–508 (2007)
Poggio, T., Vetter, T.: Recognition and structure from one 2D model view: Observations on prototypes, object classes and symmetries. A.I. Memo No. 1347, Artificial Intelligence Laboratory, Massachusetts Institute of Technology (1992)
Polikar, R., DePasquale, J., Mohammed, H.S., Brown, G., Kuncheva, L.I.: Learn++ MF: A random subspace approach for the missing feature problem. Pattern Recognition (43), 3817–3832 (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ko, A.HR., Sabourin, R. (2013). Single Classifier Based Multiple Classifications. In: Zhou, ZH., Roli, F., Kittler, J. (eds) Multiple Classifier Systems. MCS 2013. Lecture Notes in Computer Science, vol 7872. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38067-9_12
Download citation
DOI: https://doi.org/10.1007/978-3-642-38067-9_12
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-38066-2
Online ISBN: 978-3-642-38067-9
eBook Packages: Computer ScienceComputer Science (R0)