Abstract
A common way to model multi-class classification problems is by means of Error-Correcting Output Codes (ECOC). One of the main requirements of the ECOC design is that the base classifier is capable of splitting each sub-group of classes from each binary problem. In this paper, we present a novel strategy to model multi-class classification problems using sub-class information in the ECOC framework. Complex problems are solved by splitting the original set of classes into sub-classes, and embedding the binary problems in a problem-dependent ECOC design. Experimental results over a set of UCI data sets and on a real multi-class traffic sign categorization problem show that the proposed splitting procedure yields a better performance when the class overlap or the distribution of the training objects conceil the decision boundaries for the base classifier.
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
OSU-SVM-TOOLBOX, http://svm.sourceforge.net
Friedman, J., Hastie, T., Tibshirani, R.: Additive logistic regression: a statistical view of boosting. The annals of statistics 38, 337–374 (1998)
Dietterich, T., Bakiri, G.: Solving multiclass learning problems via error-correcting output codes. JAIR 2, 263–286 (1995)
Asuncion, A., Newman, D.J.: UCI Machine Learning Repository, University of California, Irvine, School of Information and Computer Sciences (2007)
Casacuberta, J., Miranda, J., Pla, M., Sanchez, S., Serra, A., Talaya, J.: On the accuracy and performance of the geomobil system. International Society for Photogrammetry and Remote Sensing (2004)
Pujol, O., Radeva, P., Vitrià, J.: Discriminant ECOC: A heuristic method for application dependent design of error correcting output codes. Trans. on PAMI 28, 1001–1007 (2006)
Pujol, O., Escalera, S., Radeva, P.: An Incremental Node Embedding Technique for Error Correcting Output Codes. Pattern Recognition (to appear)
Allwein, E., Schapire, R., Singer, Y.: Reducing multiclass to binary: A unifying approach for margin classifiers. JMLR 1, 113–141 (2002)
Escalera, S., Pujol, O., Radeva, P.: Loss-Weighted Decoding for Error-Correcting Output Codes. In: CVCRD, pp. 77–82 (October 2007)
Daume, H., Marcu, D.: A Bayesian Model for Supervised Clustering with the Dirichlet Process Prior. JMLR 6, 1551–1577 (2005)
Zhu, M., Martinez, A.M.: Subclass Discriminant Analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence 28(8), 1274–1286 (2006)
Pudil, P., Ferri, F., Novovicova, J., Kittler, J.: Floating Search Methods for Feature Selection with Nonmonotonic Criterion Functions. In: Proc. Int. Conf. Pattern Recognition, pp. 279–283 (1994)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Escalera, S., Pujol, O., Radeva, P. (2008). Sub-class Error-Correcting Output Codes. In: Gasteratos, A., Vincze, M., Tsotsos, J.K. (eds) Computer Vision Systems. ICVS 2008. Lecture Notes in Computer Science, vol 5008. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-79547-6_48
Download citation
DOI: https://doi.org/10.1007/978-3-540-79547-6_48
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-79546-9
Online ISBN: 978-3-540-79547-6
eBook Packages: Computer ScienceComputer Science (R0)