Abstract
This paper describes how the classification of imbalanced datasets through support vector machines using the boundary movement method can be easily explained in terms of a cost-sensitive learning algorithm characterized by giving each example a cost in function of its class. Moreover, it is shown that under this interpretation the boundary movement is measured in terms of the squared norm of the separator’s slopes in feature space, thus providing practical insights in order to properly choose the boundary surface shift.
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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
Papadimitriou, C.H.: Computational complexity. Addison-Wesley, Reading (1994)
Zhao, W., Chellappa, R., Phillips, P.J., Rosenfeld, A.: Face Recognition: A Literature Survey. ACM Computing Surveys 35(4), 399–458 (2003)
Tang, Y., Zhang, Y., Chawla, N.V., Krasser, S.: SVMs Modeling for Highly Imbalanced Classification. IEEE Transactions on Systems, Man, and Cybernetics, Part B 39(1), 281–288 (2009)
Chawla, N., Bowyer, K., Hall, L., Kegelmeyer, W.: SMOTE: Syntetic Minority Over-sampling Techinque. Journal of Artificial Intelligence Reserarch 16, 321–357 (2002)
Stefanowski, J., Wilk, S.: Improving Rule-Based Classifiers Induced by MODLEM by Selective Preprocessing of Imbalanced Data. In: Proceedings of the Workshop RSKD at European Conference on Machine Learning and Principles of Knowledge Discovery in Databases, Warszawa, September 17-21, pp. 54–65 (2007)
Wu, G., Chang, E.: Class-Boundary Alignment for Imbalanced Dataset Learning. In: ICML 2003 Workshop on Learning from Imbalanced Data Sets (II), pp. 49–56 (2003)
Ben-Hur, A., Horn, D., Siegelmann, H.T., Vapnik, V.: Support Vector Clustering. Journal of Machine Learning Research 2, 125–137 (2001)
Wu, G., Chang, E.Y.: Adaptive Feature-Space Conformal Transformation for Imbalanced-Data Learning. In: Proceedings of the Twentieth International Conference on Machine Learning (ICML 2003), pp. 816–823 (2003)
Cortes, C., Vapnik, V.: Support-Vector Networks. Machine Learning 20, 121–167 (1995)
Elkan, C.: The Foundations of Cost-Sensitive Learning. In: Proceedings of the 17th International Joint Conference on Artificial Intelligence, pp. 973–978 (2001)
Apolloni, B., Malchiodi, D., Natali, L.: A Modified SVM Classification Algorithm for Data of Variable Quality. In: Apolloni, B., Howlett, R.J., Jain, L. (eds.) KES 2007, Part III. LNCS (LNAI), vol. 4694, pp. 131–139. Springer, Heidelberg (2007)
Schölkopf, B., Smola, A.J.: Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. MIT Press, Cambridge (2002)
Fletcher, R.: Practical Methods of Optimisations, 2nd edn. John Wiley & Sons, Chichester (1987)
Platt, J.: Fast Training of Support Vector Machines Using Sequential Minimal Optimization. In: Schölkopf, B., Burges, C.J.C., Smola, A.J. (eds.) Advances in Kernel Methods – Support Vector Learning, pp. 185–208. MIT Press, Cambridge (1999)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Malchiodi, D. (2013). An Interpretation of the Boundary Movement Method for Imbalanced Dataset Classification Based on Data Quality. In: Apolloni, B., Bassis, S., Esposito, A., Morabito, F. (eds) Neural Nets and Surroundings. Smart Innovation, Systems and Technologies, vol 19. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35467-0_3
Download citation
DOI: https://doi.org/10.1007/978-3-642-35467-0_3
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-35466-3
Online ISBN: 978-3-642-35467-0
eBook Packages: EngineeringEngineering (R0)