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Integrating defeasible argumentation with fuzzy ART neural networks for pattern classification
Keywords:
Machine Learning, Defeasible Argumentation, Neural networks, Pattern ClassificationAbstract
Many classification systems rely on clustering techniques in which a collection of training examples is provided as an input, and a number of clusters c1,...cm modelling some concept C results as an output, such that every cluster ci is labelled as positive or negative. Given a new, unlabelled instance enew, the above classification is used to determine to which particular cluster ci this new instance belongs. In such a setting clusters can overlap, and a new unlabelled instance can be assigned to more than one cluster with conflicting labels. In the literature, such a case is usually solved non-deterministically by making a random choice. This paper presents a novel, hybrid approach to solve this situation by combining a neural network for classification along with a defeasible argumentation framework which models preference criteria for performing clustering.
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References
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[2] G. Carpenter, S. Grossberg, and D. Rosen. Fuzzy art: Fast stable learning and categorization of analog patterns by an adaptive resonance system. Neural Networks, 4:759–771, 1991.
[3] C. I. Chesnevar, J. Dix, F. Stolzenburg, and G. R. Simari. Relating Defeasible and Normal Logic Programming through Transformation Properties. Theoretical Computer Science, 290(1):499–529, 2003.
[4] C. I. Chesnevar, A. Maguitman, and R. Loui. Logical Models of Argument. ACM Computing Surveys, 32(4):337–383, Dec. 2000.
[5] C. I. Chesnevar, G. R. Simari, and A. García. Pruning Search Space in Defeasible Argumentation. In Procs. of Workshop on Advances & Trends in AI, pages 46–55. XX Intl. Conf. of the SCCC, Chile, Nov. 2000.
[6] I. Davidson. Visualizing Clustering Results. In Proc. of 2nd SIAM International Conference on Data Mining, Arlington VA, USA. SIAM, 2002.
[7] A. J. García and G. R. Simari. Defeasible Logic Programming: An Argumentative Approach. Theory and Practice of Logic Programming, 4(1):95–138, 2004.
[8] S. A. Gómez and C. I. Chesnevar. Integrating Defeasible Argumentation and Machine Learning Techniques: A Preliminary Report. In Procs. V Workshop of Researchers in Comp. Science, pages 320–324, 2003.
[9] S. A. Gómez and L. Lanzarini. Querando!: Un agente de filtrado de documentos web. Procs. of the VII Argentinean Conf. in Computer Science (CACIC), pages 1205–1217, 2001. 1 E.g. http://www.ics.uci.edu/∼mlearn/MLRepository.html
[10] T. Honkela. Self-organizing maps in Natural Language Processing. PhD thesis, Helsinky Univ., 1997.
[11] A. Hunter. Hybrid argumentation systems for structured news reports. Knowledge Engineering Review, (16):295–329, 2001.
[12] K. Inoue and Y. Kudoh. Learning Extended Logic Programs. In Proc. of the 15th IJCAI (vol.1), pages 176–181. Morgan Kaufmann, 1997.
[13] B. Johnston and G. Governatori. An algorithm for the induction of defeasible logic theories from databases. In Proc. of the 14th Australasian Database Conference (ADC2003), pages 75–83, 2003.
[14] Y. Lashkari, M. Metral, and P. Maes. Collaborative interface agents. In Readings in Agents, pages 111–116. Morgan Kaufmann, 1997.
[15] P. Lavoie, J. Crespo, and Y. Savaria. Generalization, discrimination, and multiple categorization using adaptive resonance theory. IEEE Transactions on Neural Networks, 10(4):757–767, July 1999.
[16] J. Lubkin and G. Cauwenberghs. VLSI Implementation of Fuzzy Adaptive Resonance and Learning Vector Quantization. Analog Integrated Circuits and Signal Processing, 23:1–10, 2001.
[17] H. Prakken and G. Sartor. The role of logic in computational models of legal argument - a critical survey. In A. Kakas and F. Sadri, editors, Computational Logic: Logic Programming and Beyond, pages 342–380. Springer, 2002.
[18] H. Prakken and G. Vreeswijk. Logical Systems for Defeasible Argumentation. In D. Gabbay and F.Guenther, editors, Handbook of Phil. Logic, pages 219–318. Kluwer Academic Publishers, 2002.
[19] V. Rao and H. Rao. C++ Neural Networks and Fuzzy Logic, Second Edition. MIS Press, 1995.
[20] E. Rasmussen. Clustering algorithms. In W. Frakes and R. Baeza-Yates, editors, Information Retrieval, pages 419–442. Prentice Hall, 1992.
[21] J. Shavlik and G. Towell. An approach to combining explanation-based and neural learning algorithms. Connection Science, 1(3):233–255, 1989.
[22] G. R. Simari and R. P. Loui. A Mathematical Treatment of Defeasible Reasoning and its Implementation. Artificial Intelligence, 53:125–157, 1992.
[23] F. Stolzenburg, A. García, C. I. Chesnevar, and G. R.Simari. Computing Generalized Specificity. Journal of Non-Classical Logics, 13(1):87–113, 2003.
[24] B. Verheij. Artificial argument assistants for defeasible argumentation. Artificial Intelligence Journal, 150:291–324, 2003.
[25] P. D. Wasserman. Neural Computing. Theory and Practice. Van Nostrand Reinhold, 1989.
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Published
2004-04-01
How to Cite
Gómez, S. A., & Chesñevar, C. I. (2004). Integrating defeasible argumentation with fuzzy ART neural networks for pattern classification. Journal of Computer Science and Technology, 4(01), p. 45–51. Retrieved from https://journal.info.unlp.edu.ar/JCST/article/view/913
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