On Decomposition for Incomplete Data

• Autorzy: Latkowski R.
• Języki publikacji: EN

Abstrakty

EN

In this paper we present a method of data decomposition to avoid the necessity of reasoning on data with missing attribute values. This method can be applied to any algorithm of classifier induction. The original incomplete data is decomposed into data subsets without missing values. Next, methods for classifier induction are applied to these sets. Finally, a conflict resolving method is used to obtain final classification from partial classifiers. We provide an empirical evaluation of the decomposition method accuracy and model size with use of various decomposition criteria on data with natural missing values. We present also experiments on data with synthetic missing values to examine the properties of proposed method with variable ratio of incompleteness.

Słowa kluczowe

EN

data mining; rough sets; missing attribute values

• Wydawca: IOS Press
• Czasopismo: Fundamenta Informaticae
• Rocznik: 2003
• Tom: Vol. 54, nr 1
• Strony: 1--16
• Opis fizyczny: tab., biblogr. 25 poz.

Twórcy

autor

Latkowski R.

• Institute of Computer Science, Warsaw University ul. Banacha 2 02-097 Warsaw, Poland,

Bibliografia

• [1] Bazan, J. G., Szczuka, M. S., Wróblewski, J.: A New Version of Rough Set Exploration System, Rough Sets and Current Trends in Computing, 3rd International Conference, RSCTC 2002, Malvern, PA, USA, October 14-16, 2002, Proceedings (J. J. Alpigini, J. F. Peters, A. Skowron, N. Zhong, Eds.), Springer, 2002.
• [2] Birk, A., Coradeschi, S., Tadokoro, S., Eds.: RoboCup 2001: Robot Soccer World Cup V, Springer, 2002.
• [3] Dash, M., Liu, H.: Feature Selection for Classification, Intelligent Data Analysis, 1(3), 1997.
• [4] Freund, Y., Schapire, R. E.: A decision-theoretic generalization of on-line learning and an application to boosting, EuroCOLT ’95 (P. M. B. Vitányi, Ed.), Springer, 1995.
• [5] Fujikawa, Y., Ho, T. B.: Cluster-Based Algorithms for Dealing with Missing Values, PAKDD-2002 (M.-S. Chen, P. S. Yu, B. Liu, Eds.), Springer, 2002.
• [6] Grzymała-Busse, J. W., Hu, M.: A Comparison of Several Approaches to Missing Attribute Values in Data Mining, Rough Sets and Current Trends in Computing, Second International Conference, RSCTC 2000 Banff, Canada, October 16-19, 2000, Revised Papers (W. Ziarko, Y. Y. Yao, Eds.), Springer, 2001.
• [7] Grzymała-Busse, J. W., Wang, A. Y.: Modified algorithms LEM1 and LEM2 for rule induction from data with missing attribute values, Proceedings of 5th Workshop on Rough Sets and Soft Computing (RSSC’97) at the 3rd Joint Conference on Information Sciences, Research Triangle Park (NC, USA), 1997.
• [8] Komorowski, J., Pawlak, Z., Polkowski, L., Skowron, A.: Rough Sets: A Tutorial, Rough Fuzzy Hybridization. A New Trend in Decision Making (S. K. Pal, A. Skowron, Eds.), Springer, Singapore, 1999.
• [9] Kryszkiewicz, M.: Properties of Incomplete Information Systems in the Framework of Rough Sets, in: Polkowski and Skowron [18], 422-450.
• [10] Latkowski, R.: Application of Data Decomposition to Incomplete Information Systems, Intelligent Information Systems XI, 2002, Sopot, Poland (M. A. Kłopotek, S. T. Wierzcho´n, Eds.), Physica-Verlag, 2002.
• [11] Lim, T.: Missing covariate values and classification trees, http://www.recursive-partitioning.com/mv.shtml, Recursive-Partitioning.com, 2000.
• [12] Michalewicz, Z.: Genetic Algorithms + Data Structures = Evolution Progams, Springer, 1996.
• [13] Nguyen, H. S., Nguyen, S. H.: From Optimal Hyperplanes to Optimal Decision Trees, RSFD’96 (S. Tsumoto, S. Kobayashi, T. Yokomori, H. Tanaka, A. Nakamura, Eds.), Tokyo University, 1996.
• [14] Nguyen, H. S., Nguyen, S. H.: Discretization Methods in Data Mining, in: Polkowski and Skowron [18], 451-482.
• [15] Nguyen, S. H.: Regularity Analysis and its Application in Data Mining, Ph.D. Thesis, Warsaw University, Faculty of Mathematics, Computer Science and Mechanics, 1999.
• [16] Nguyen, S. H., Skowron, A., Synak, P.: Discovery of data patterns with applications to decomposition and classification problems, in: Polkowski and Skowron [19], 55-97.
• [17] Pawlak, Z.: Rough Sets, International Journal of Computer and Information Sciences, 11, 1982, 341-356.
• [18] Polkowski, L., Skowron, A., Eds.: Rough Sets in Knowledge Discovery 1: Methodology and Applications, Physica-Verlag, 1998.
• [19] Polkowski, L., Skowron, A., Eds.: Rough Sets in Knowledge Discovery 2: Applications, Case Studies and Software Systems, Physica-Verlag, 1998.
• [20] Quinlan, J. R.: Unknown Attribute Values in Induction, Proceedings of the Sixth International Machine Learning Workshop (A. M. Segre, Ed.), Morgan Kaufmann, 1989.
• [21] Skowron, A.: Synthesis of Adaptive Decision Systems from Experimental Data, SCAI 1995 (A. Aamodt, J. H. Komorowski, Eds.), IOS Press, 1995.
• [22] Skowron, A., Rauszer, C.: The Discernibility Matrices and Functions in Information Systems, Intelligent Decision Support. Handbook of Applications and Advances in Rough Sets Theory (R. Słowiński, Ed.), Kluwer, Dordrecht, 1992.
• [23] Stefanowski, J.: On rough set based approaches to induction of decision rules, in: Polkowski and Skowron [18], 500-529.
• [24] Stefanowski, J., Tsoukiàs, A.: Incomplete Information Tables and Rough Classification, International Journal of Computational Intelligence, 17(3), August 2001, 545-566.
• [25] Weiss, S. M., Indurkhya, N.: Lightweight Rule Induction, Proceedings of the International Conference on Machine Learning ICML’2000, 2000.