Application of Argumentation for Improving the Classification Accuracy in Inductive Concept Formation
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.
[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).
[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).
@article{"International Journal of Information, Control and Computer Sciences:70022", author = "Vadim Vagin and Marina Fomina and Oleg Morosin", title = "Application of Argumentation for Improving the Classification Accuracy in Inductive Concept Formation", 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.", volume = "9", number = "5", pages = "1304-9", }
