Abstract
In this paper we introduce a preprocessing method for safety-related applications. Since we concentrate on scenarios with highly unbalanced misclassification costs, we briefly discuss a variation of multiple-instance learning (MIL) and recall soft margin hyperplane classifiers; in particular the principle of a support vector machine (SVM). According to this classifier, we present a training set selection method for learning quasilinear SVMs which guarantee both high accuracy and model complexity to a lower degree. We conclude with annotating on a real-world application and potential extensions for future research in this domain.
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References
Andrews, S., Tsochantaridis, I., Hofmann, T.: Support vector machines for multiple-instance learning. In: Becker, S., Thrun, S., Obermayer, K. (eds.) Proceedings of the NIPS-2002 Conference. Advances in Neural Information Processing Systems, MA, USA, vol. 15, pp. 561–568. MIT Press, Cambridge (2003)
Chaves, A., Vellasco, M.B.R., Tanscheit, R.: Fuzzy rule extraction from support vector machines. In: Nedjah, N., Mourelle, L., Abraham, A., Köppen, M. (eds.) Proceedings of Fifth International Conference on Hybrid Intelligent Systems (HIS2005, Rio de Janeiro, Brazil), pp. 335–340. IEEE Computer Society Press, Los Alamitos (2005)
Chen, Y., Wang, J.Z.: Support vector learning for fuzzy rule-based classification systems. IEEE FS 11(6), 716–728 (2003)
Chen, Y., Wang, J.Z.: Image categorization by learning and reasoning with regions. J. Mach. Learn Res. 5, 913–939 (2004)
Dietterich, T.G., Lathrop, R.H., Lozano-Pérez, T.: Solving the multiple instance problem with axis-parallel rectangles. Artificial Intelligence 89(1-2), 31–71 (1997)
Maron, O., Lozano-Pérez, T.: A framework for multiple-instance learning. In: Jordan, M.I., Kearns, M.J., Solla, S.A. (eds.) Proceedings of the NIPS-1997 Conference. Advances in Neural Information Processing Systems, vol. 10, pp. 570–576. MIT Press, Cambridge (1998)
Maron, O., Ratan, A.: Multiple-instance learning for natural scene classification. In: Shavlik, J. (ed.) Proceedings of the 15th International Conference on Machine Learning (ICML-98, Madison, WI), pp. 341–349. Morgan Kaufmann, San Francisco (1998)
Moewes, C.: Application of Support Vector Machines to Discriminate Vehicle Crash Events. Master’s Thesis. FIN, University of Magdeburg (2007)
Otte, C., Nusser, S., Hauptmann, W.: Machine learning methods for safety-related domains: Status and perspectives. In: Proceedings of the Symposium on Fuzzy Systems in Computer Science (FSCS 2006, Magdeburg, Germany), pp. 139–148 (2006)
Papadimitriou, S., Terzidis, C.: Efficient and interpretable fuzzy classifiers from data with support vector learning. Intell. Data Anal. 9(6), 527–550 (2005)
Schölkopf, B., Smola, A.J.: Learning with Kernels. In: Support Vector Machines, Regularization, Optimization, and Beyond, MIT Press, Cambridge (2002)
Vapnik, V., Chervonenkis, A.: Theory of Pattern Recognition [in Russian], Nauka, Moscow; Wapnik, W., Tscherwonenkis, A.: Theorie der Zeichenerkennung. Akademie–Verlag, Berlin (1979) (German Translation)
Xu, X.: Statistical Learning in Multiple Instance Problems. Master’s Thesis. Department of Computer Science, University of Waikato (2003)
Yang, C., Lozano-Pérez, T.: Image database retrieval with multiple-instance learning techniques. In: Proceedings of the 16th International Conference on Data Engineering (ICDE, San Diego, CA), pp. 233–243 (2000)
Zhang, Q., Goldman, S.A., Yu, W., Fritts, J.E.: Content-based image retrieval using multiple-instance learning. In: Sammut, C., Hoffmann, A.G. (eds.) The 19th International Conference on Machine Learning (ICML 2002, Sydney, Australia), pp. 682–689. Morgan Kaufmann, San Francisco (2002)
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Moewes, C., Otte, C., Kruse, R. (2008). Tackling Multiple-Instance Problems in Safety-Related Domains by Quasilinear SVM. In: Dubois, D., Lubiano, M.A., Prade, H., Gil, M.Á., Grzegorzewski, P., Hryniewicz, O. (eds) Soft Methods for Handling Variability and Imprecision. Advances in Soft Computing, vol 48. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85027-4_49
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DOI: https://doi.org/10.1007/978-3-540-85027-4_49
Publisher Name: Springer, Berlin, Heidelberg
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