Abstract
In this paper, we deal with two important issues regarding possibilistic network-based classifiers. The first issue addresses the reject option in possibilistic network-based classifiers. We first focus on simple threshold-based reject rules and provide interpretations for the ambiguity and distance reject then introduce a third reject kind named incompleteness reject occurring when the inputs are missing or incomplete. The second important issue we address is the one of concept drift. More specifically, we propose an efficient solution for revising a possibilistic network classifier with new information.
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References
Ben-Amor, N., Benferhat, S., Mellouli, K.: A two-steps algorithm for min-based possibilistic causal networks. In: Benferhat, S., Besnard, P. (eds.) ECSQARU 2001. LNCS (LNAI), vol. 2143, pp. 266–277. Springer, Heidelberg (2001)
Benferhat, S., Tabia, K.: An efficient algorithm for naive possibilistic classifiers with uncertain inputs. In: Greco, S., Lukasiewicz, T. (eds.) SUM 2008. LNCS (LNAI), vol. 5291, pp. 63–77. Springer, Heidelberg (2008)
Benferhat, S., Tabia, K., Sedki, K.: On analysis of unicity of jeffrey’s rule of conditioning in a possibilistic framework. In: 11th International Symposium on Artificial Intelligence and Mathematics, Florida, USA (2010)
Borgelt, C., Gebhardt, J.: A naive bayes style possibilistic classifier. In: Proceedings of the 7th European Congress on Intelligent Techniques and Soft Computing, Verlag Mainz, Aachen, Germany (1999)
Borgelt, C., Kruse, R.: Graphical Models: Methods for Data Analysis and Mining. John Wiley and Sons, Inc., USA (2002)
Chow, C.: On optimum recognition error and reject tradeoff. IEEE Transactions on Information Theory 16(1), 41–46 (1970)
Cortes, C., Vapnik, V.: Support-vector networks. Machine Learning 20(3), 273–297 (1995)
Dubois, D., Hüllermeier, E., Prade, H.: Flexible control of case-based prediction in the framework of possibility theory. In: 5th European Workshop on Advances in Case-Based Reasoning, London, UK, pp. 61–73 (2000)
Dubois, D., Prade, H.: Possibility theory. Plenium Press, New-York (1988)
Dubois, D., Prade, H.: A synthetic view of belief revision with uncertain inputs in the framework of possibility theory. Int. J. of Approximate Reasoning 17(2-3), 295–324 (1997)
Frélicot, C.: On unifying probabilistic/fuzzy and possibilistic rejection-based classifiers. In: Amin, A., Pudil, P., Dori, D. (eds.) SPR 1998 and SSPR 1998. LNCS, vol. 1451, pp. 736–745. Springer, Heidelberg (1998)
Friedman, N., Geiger, D., Goldszmidt, M.: Bayesian network classifiers. Machine Learning 29(2-3), 131–163 (1997)
Gebhardt, J.: Knowledge revision in markov networks. Mathware & Soft Computing 11, 93–107 (2004)
Ha, T.M.: The optimum class-selective rejection rule. IEEE Trans. Pattern Anal. Mach. Intell. 19, 608–615 (1997)
Haouari, B., Ben Amor, N., Elouedi, Z., Mellouli, K.: Naïve possibilistic network classifiers. Fuzzy Sets and Systems 160(22), 3224–3238 (2009)
Hisdal, E.: Conditional possibilities independence and non interaction. Fuzzy Sets and Systems, 283–297 (1978)
Jeffrey, R.C.: The Logic of Decision. McGraw Hill, NY (1965)
Le Capitaine, H., Frelicot, C.: Classification with reject options in a logical framework: a fuzzy residual implication approach. In: Proc. IFSA/EUSFLAT Conference, Lisbonne Portugal, pp. 855–860 (2009)
Le Capitaine, H., Frelicot, C.: An Optimum Class-Rejective Decision Rule and Its Evaluation. In: Proceedings of the 2010 20th International Conference on Pattern Recognition, Istanbul, Turquie, pp. 3312–3315. IEEE Computer Society, Los Alamitos (2010)
Leray, P., Zaragoza, H., d’Alch-Buc, F.: Pertinence des mesures de confiance en classification. In: 12eme Congres Francophone AFRIF-AFIA Reconnaissance des Formes et Intelligence Articifielle, Paris, France, pp. 267–276 (2000)
Pearl, J.: Probabilistic reasoning in intelligent systems: networks of plausible inference. Morgan Kaufmann Publishers Inc., San Francisco (1988)
Ross Quinlan, J.: C4.5: Programs for Machine Learning. Morgan Kaufmann, San Francisco (1993)
Tsymbal, A.: The problem of concept drift: Definitions and related work. Technical report, TCD-CS-2004-15, Trinity College Dublin, Ireland (2004)
Widmer, G., Kubat, M.: Learning in the presence of concept drift and hidden contexts. Mach. Learn. 23, 69–101 (1996)
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Tabia, K. (2011). Possibilistic Network-Based Classifiers: On the Reject Option and Concept Drift Issues. In: Benferhat, S., Grant, J. (eds) Scalable Uncertainty Management. SUM 2011. Lecture Notes in Computer Science(), vol 6929. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23963-2_36
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DOI: https://doi.org/10.1007/978-3-642-23963-2_36
Publisher Name: Springer, Berlin, Heidelberg
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