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Application of Argumentation for Improving the Classification Accuracy in Inductive Concept Formation
Authors: Vadim Vagin, Marina Fomina, Oleg Morosin
Abstract:
This paper contains the description of argumentation approach for the problem of inductive concept formation. It is proposed to use argumentation, based on defeasible reasoning with justification degrees, to improve the quality of classification models, obtained by generalization algorithms. The experiment’s results on both clear and noisy data are also presented.Keywords: Argumentation, justification degrees, inductive concept formation, noise, generalization.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1106593
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[1] N. Vagin, E. Golovina, A. Zagoryanskaya and M. Fomina. Exact and Plausible Inference in Intelligent Systems./V. Vagin and D. Pospelov, Eds., Moscow: FizMatLit, 2008, p. 714 (in Russian).
[2] Quinlan J. R. Induction of Decision Trees. // Machine Learning, vol. 1, 1986, pp. 81-106.
[3] Quinlan J.R., C4.5: Programs for Machine Learning. San Francisco: Morgan Kaufmann Publishers Inc., 1993, p. 302.
[4] P. Clark and R. Boswell., Rule Induction with CN2: Some Recent Improvements. // Machine Learning - Proceedings of the Fifth European Conference (ESWL-91), Berlin, Springer-Verlag, 1991, pp. 151-163.
[5] P.Clark, T.Niblett, The CN2 Induction Algorithm, // Machine Learning , vol. 3, 1989, pp. 261-283.
[6] Z. Pawlak, Rough sets and intelligent data analysis. // Information Sciences, vol. 147, no. 1, 2002, pp. 1-12.
[7] Quinlan J.R., Improved Use of Continuous Attributes in C 4.5. // Journal of Artifical Intelligence Research, vol.4, 1996, pp. 77-90.
[8] J. Komorowski, Z. Pawlak, L. Polkowski and A. Skowron, Rough Sets: A Tutorial, Springer-Verlag, Singapore,1999, pp. 3-98.
[9] S. Nguyen and H. Nguyen, Some efficient algorithms for rough set methods // Proc. of Information Processing and Management of Uncertainty on Knowledge-Based Systems (IPMU-96), Spain, vol. III, 1996, pp. 1451-1456.
[10] Bazan J. A comparison of dynamic and non-dynamic rough set methods for extracting laws from decision tables / Rough Sets in Knowledge Discovery 1: Methodology and Applications // Polkowski L., Skowron A. (eds. ), Physica-Verlag, 1998.
[11] Vagin, M. Fomina and A. Kulikov, The Problem of Object Recognition in the Presence of Noise in Original Data // 10th Scandinavian Conference on Artificial Intelligence SCAI, 2008, pp. 60-67.
[12] Mookerjee, M. Mannino and R. Gilson, Improving the Performance Stability of Inductive Expert Systems under Input Noise, // Information Systems Research, vol. 6, no. 4, 1995, pp. 328-356.
[13] V. Vagin and M. Fomina, Problem of Knowledge Discovery in Noisy Databases // International Journal of Machine Learning and Cybernetics, vol. 2, no. 3, 2011, pp. 135-145.
[14] V. Vagin and M. Fomina, Methods and Algorithms of Information Generalization in Noisy Databases // Advances in Soft Computing. 9th Mexican Intern. Conference on AI, MICAI, Pachuca, 2010, pp. 44-55.
[15] Santiago Ontanon and Enric Plaza, Multiagent Inductive Learning: an Argumentation-based Approach // Proc. ICML-2010, 27th International Conference on Machine Learning, 2010, pp. 839-846.
[16] A. Bondarenko, P. Dung, R. Kowalski, F. Toni and A. Bondarenko, An Abstract Argumentation-Theoretic Framework for Defeasible Reasoning. // Artificial Intelligence, vol. 93, no. 1-2, 1997, pp. 63-101.
[17] 15. F. Lin and Y. Shoham, Argument Systems. A Uniform Basis for Nonmonotonic Reasoning, // Principles of Knowledge Representation and Reasoning, San Mateo, CA, Morgan Kaufmann, 1989, pp 245-255.
[18] G. Vreeswijk, Abstract Argumentation Systems // Artificial Intelligence, vol. 90, 1997, pp. 225-279.
[19] J. Pollock, Oscar – a General Purpose Defeasible Reasoner. // Journal of Nonclassical Logics, vol. 6, 1996, pp. 89-113.
[20] G. Betz, On Degrees of Justification. // Erkenntnis, vol. 2, 2012, pp. 237-272.
[21] Pollock J.L. Defeasible reasoning with variable degrees of justification, // Artificial Intelligence, vol. 133, 2001, pp. 233-282.
[22] R. Haenni, J. Kohlas and N. Lehmann, Probabilistic Argumentation Systems. // Handbook of Defeasible Reasoning and Uncertainty Management Systems, vol. 5: Algorithms for Uncertainty and Defeasible Reasoning, Dordrecht, Kluwer, 1999, pp. 221-287.
[23] C. Merz and M. P., UCI Repository of Machine Learning Datasets. // Information and Computer Science University of California, 1998. (Online). Available: http://archive.ics.uci.edu/ml/. (Accessed 10.03.2014).