Abstract
A method for constructing classification (decision) systems is presented. The use of decision rules derived using rough set methods as new attributes is considered. Neural networks are applied as a tool for construction of classifier over reconstructed dataset. Possible profits of such an approach are briefly presented together with results of preliminary experiments.
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References
Agrawal, R., Manilla, H., Srikant, R., Toivonen, H., Verkamo, I.: Fast Discovery of Association Rules. In: Proceedings of the Advances in Knowledge Discovery and Data Mining, pp. 307–328. AAAI-Press/MIT Press (1996)
Arbib, M.A. (ed.): The Handbook of Brain Theory and Neural Networks. MIT Press, Cambridge (1995)
Bazan, J.: A Comparison of Dynamic and non-Dynamic Rough Set Methods for Extracting Laws from Decision Tables. In: Skowron, A., Polkowski, L. (eds.) Rough Sets in Knowledge Discovery 1, pp. 321–365. Physica Verlag, Heidelberg (1998)
Bazan, J.: Approximate reasoning methods for synthesis of decision algorithms (in Polish), Ph. D. Thesis, Department of Math., Comp. Sci. and Mechanics, Warsaw University, Warsaw (1998)
Hoa, N.S., Son, N.H.: Discretization Methods in Data Mining. In: Skowron, A., Polkowski, L. (eds.) Rough Sets in Knowledge Discovery 1, pp. 451–482. Physica Verlag, Heidelberg (1998)
Karayannis, N.B., Venetsanopoulos, A.N.: Artificial Neural Networks: Learning algorithms, Performance Evaluation and Applications. Kluwer, Dordrecht (1993)
Michalski, R., Tecuci, G.: Machine Learning IV: A Multistrategy Approach. Morgan-Kaufmann, San Francisco (1994)
Michie, D., Spiegelhalter, D.J., Taylor, C.C.: Machine Learning, Neural and Statistical Classification. Ellis Horwood, London (1994)
Pawlak, Z.: Rough Sets: Theoretical Aspects of Reasoning about Data. Kluwer, Dordrecht (1991)
Szczuka, M., Wojdyllo, P.: Neuro-Wavelet Classifiers for EEG Signals Based on Rough Set Methods. Submitted to Neurocomputing (June 1999)
Wojdyllo, P.: Wavelets, Rough Sets and Artificial Neural Networks in EEG Analysis. In: Polkowski, L., Skowron, A. (eds.) RSCTC 1998. LNCS (LNAI), vol. 1424, pp. 444–449. Springer, Heidelberg (1998)
Wroblewski, J.: Covering with Reducts - A Fast Algorithm for Rule Generation. In: Polkowski, L., Skowron, A. (eds.) RSCTC 1998. LNCS (LNAI), vol. 1424, pp. 402–407. Springer, Heidelberg (1998)
Ziarko, W.: Variable Precision Rough Set Model. Journal of Computer and System Sciences 40, 39–59 (1993)
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Szczuka, M.S. (1999). Rules as Attributes in Classifier Construction. In: Zhong, N., Skowron, A., Ohsuga, S. (eds) New Directions in Rough Sets, Data Mining, and Granular-Soft Computing. RSFDGrC 1999. Lecture Notes in Computer Science(), vol 1711. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-48061-7_60
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DOI: https://doi.org/10.1007/978-3-540-48061-7_60
Publisher Name: Springer, Berlin, Heidelberg
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