Abstract
Multiclass classification using Machine Learning techniques consists of inducing a function f(x) from a training set composed of pairs (x i ,y i ) where y i ∈ {1,2,...,k}. Some learning methods are originally binary, being able to realize classifications where k = 2. Among these one can mention Support Vector Machines. This paper presents a comparison of methods for multiclass classification using SVMs. The techniques investigated use strategies of dividing the multiclass problem into binary subproblems and can be extended to other learning techniques. Results indicate that the use of Directed Acyclic Graphs is an efficient approach in generating multiclass SVM classifiers.
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References
Allwein, E.L., Shapire, R.E., Singer, Y.: Reducing Multiclass to Binary: a Unifying Approach for Margin Classifiers. In: Proceedings of the 17th International Conference on Machine Learning, pp. 9–16. Morgan Kaufmann, San Francisco (2000)
Collobert, R., Bengio, S.: SVMTorch: Support vector machines for large scale regression problems. Journal of Machine Learning Research 1, 143–160 (2001)
Cristianini, N., Taylor, J.S.: An Introduction to Support Vector Machines. Cambridge University Press, Cambridge (2000)
Dietterich, T.G., Bariki, G.: Solving Multiclass Learning Problems via Error-Correcting Output Codes. Journal of Artificial Intelligence Research 2, 263–286 (1995)
Cortes, C., Vapnik, V.N.: Support Vector Networks. Machine Learning 20, 273–296 (1995)
Haykin, S.: Neural Networks - A Compreensive Foundation. Prentice-Hall, New Jersey (1999)
Hsu, C.-W., Lin, C.-J.: A comparison of methods for multi-class support vector machines. IEEE Transactions on Neural Networks 13, 415–425 (2002)
Kreßel, U.: Pairwise Classification and Support Vector Machines. In: Scholkopf, B., Burges, C.J.C., Smola, A.J. (eds.) Advances in Kernel Methods - Support Vector Learning, pp. 185–208. MIT Press, Cambridge (1999)
Mayoraz, E., Alpaydm, E.: Support Vector Machines for Multi-class Classification. Technical Report IDIAP-RR-98-06, Dalle Molle Institute for Perceptual Artificial Intelligence, Martigny, Switzerland (1998)
Mitchell, T.: Machine Learning. McGraw-Hill, New York (1997)
Müller, K.R., et al.: An Introduction to Kernel-based Learning Algorithms. IEEE Transactions on Neural Networks 12(2), 181–201 (2001)
Nadeau, C., Bengio, Y.: Inference for the Generalization Error. Machine Learning 52(3), 239–281 (2003)
Pedersen, A.G., Nielsen, H.: Neural Network Prediction of Translation Initiation Sites in Eukaryotes: Perspectives for EST and Genome Analysis. In: Proceedings of ISMB 1997, pp. 226–233 (1997)
Platt, J.C., Cristianini, N., Shawe-Taylor, J.: Large Margin DAGs for Multiclass Classification. In: Solla, S.A., Leen, T.K., Müller, K.-R. (eds.) Advances in Neural Information Processing Systems, vol. 12, pp. 547–553. MIT Press, Cambridge (2000)
Smola, A.J., et al.: Introduction to Large Margin Classifiers. In: Advances in Large Margin Classifiers, ch. 1, pp. 1–28. MIT Press, Cambridge (1999)
University of California Irvine: UCI benchmark repository - a huge collection of artificial and real-world datasets, http://www.ics.uci.edu/~mlearn
Vapnik, V.N.: The Nature of Statistical Learning Theory. Springer, Heidelberg (1995)
Weston, J., Watkins, V.: Multi-class Support Vector Machines. Technical Report CSD-TR-98-04, Department of Computer Science, University of London (1998)
Zell, A., et al.: SNNS - Stuttgart Neural Network Simulator. Technical Report 6/95, Institute for Parallel and Distributed High Performance Systems (IPVR), University of Stuttgart (1995)
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Lorena, A.C., de Carvalho, A.C.P.L.F. (2004). Comparing Techniques for Multiclass Classification Using Binary SVM Predictors. In: Monroy, R., Arroyo-Figueroa, G., Sucar, L.E., Sossa, H. (eds) MICAI 2004: Advances in Artificial Intelligence. MICAI 2004. Lecture Notes in Computer Science(), vol 2972. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24694-7_28
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DOI: https://doi.org/10.1007/978-3-540-24694-7_28
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-21459-5
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