Abstract
In a context of multiple classifiers, a calibration step based on logistic regression is usually used to independently transform each classifier output into a probability distribution, to be then able to combine them. This calibration has been recently refined, using the evidence theory, to better handle uncertainties. In this paper, we propose to use this logistic-based calibration in a multivariable scenario, i.e., to consider jointly all the outputs returned by the classifiers, and to extend this approach to the evidential framework. Our evidential approach was tested on generated and real datasets and presents several advantages over the probabilistic version.
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References
Bache, K., Lichman, M.: UCI machine learning repository (2013). http://archive.ics.uci.edu/ml
Chang, C-C., Lin, C-J.: LIBSVM: A library for support vector machines. Trans. Intel. Syst. Technol. 2, 27:1–27:27 (2011). Software, http://www.csie.ntu.edu.tw/~cjlin/libsvm
Dempster, A.P.: New methods for reasoning towards posterior distributions based on sample data. Ann. Math. Stat. 37(2), 355–374 (1966)
Denœux, T.: Analysis of evidence-theoretic decision rules for pattern classification. Pattern Recogn. 30(7), 1095–1107 (1997)
Hosmer, D.W., Lemeshow, S.: Applied Logistic Regression. Wiley, New York (2004)
Kanjanatarakul, O., Denœux, T., Sriboonchitta, S.: Prediction of future observations using belief functions: a likelihood-based approach. Int. J. Approximate Reasoning 72, 71–94 (2015)
Kanjanatarakul, O., Sriboonchitta, S., Denœux, T.: Forecasting using belief functions: an application to marketing econometrics. Int. J. Approximate Reasoning 55(5), 1113–1128 (2014)
Platt, J.C.: Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. Adv. in large margin classifiers 10(3), 61–74 (1999)
Shafer, G.: A Mathematical Theory Of Evidence. Princeton University Press, Princeton (1976)
Smets, P., Kennes, R.: The transferable belief model. Artif. Intell. 66, 191–243 (1994)
Xu, P., Davoine, F., Zha, H., Denœux, T.: Evidential calibration of binary SVM classifiers. Int. J. Approximate Reasoning 72, 55–70 (2016)
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Minary, P., Pichon, F., Mercier, D., Lefevre, E., Droit, B. (2017). Evidential Joint Calibration of Binary SVM Classifiers Using Logistic Regression. In: Moral, S., Pivert, O., Sánchez, D., Marín, N. (eds) Scalable Uncertainty Management. SUM 2017. Lecture Notes in Computer Science(), vol 10564. Springer, Cham. https://doi.org/10.1007/978-3-319-67582-4_30
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DOI: https://doi.org/10.1007/978-3-319-67582-4_30
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