Abstract
This paper presents a new approach for inducing decision trees based on Variable Precision Rough Set Model(VPRSM). From the Rough Set theory point of view, in the process of inducing decision trees, some methods, such as information entropy based methods, emphasize the effect of class distribution. The more unbalanced the class distribution is, the more favorable it is. Whereas the Rough Set based approaches for inducing decision trees emphasize the effect of certainty. The more certain it is, the better it is. Two main concepts, i.e. variable precision explicit region, variable precision implicit region, and the process for inducing decision trees are introduced and discussed in the paper. The comparison between the presented approach and C4.5 on some data sets from the UCI Machine Learning Repository is also reported.
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References
Pawlak, Z.: Rough sets. International J. Comp. Inform. Science 11, 341–356 (1982)
Jerzy, W., GrZymala-Busse, Z.W.: Data mining and rough set theory. Communications of the ACM 43, 108–109 (2000)
Pawlak, Z.: Rough set approach to multi-attribute decision analysis. European Journal of Operational Research 72, 443–459 (1994)
Pawlak, Z., Wang, S.K.M., Ziarko, W.: Rough sets: probabilistic versus deterministic approach. Int. J. Man-Machine Studies 29, 81–95 (1988)
Moshkov, M.J.: Time Complexity of Decision Trees. In: Peters, J.F., Skowron, A. (eds.) Transactions on Rough Sets III. LNCS, vol. 3400, pp. 244–459. Springer, Heidelberg (2005)
JinMao, W.: Rough Set Based Approach to Selection of Node. International Journal of Computational Cognition 1, 25–40 (2003)
Mingers, J.: An empirical comparison of pruning methods for decision-tree induction. Machine Learning 4, 319–342 (1989)
Quinlan, J.R., Rivest, R.: Inferring decision trees using the minimum description length principle. Information and Computation 80, 227–248 (1989)
Quinlan, J.R.: Introduction of Decision Trees. Machine Learning 3, 81–106 (1986)
Ziarko, W.: Imprecise Concept Learning within a Growing Language. In: Proceedings of the sixth international workshop on Machine learning 1989, Ithaca, New York, United States, pp. 314–319 (1989)
Jian, L., Da, Q., Chen, W.: Variable Precision Rough Set and a Fuzzy Measure of Knowledge Based on Variable Precision Rough Set. Journal of Southeast University (English Edition) 18, 351–355 (2002)
Kryszkiewicz, M.: Maintenance of reducts in the variable precision rough set model. In: 1995 ACM Computer Science Conference (CSS 1995), pp. 355–372 (1995)
Ziarko, W.: Probabilistic Decision Tables in the Variable Precision Rough Set Model. Computational Intelligence 17, 593–603 (2001)
Ziarko, W.: Variable precision rough set model. Journal of Computer and System Sciences 46, 39–59 (1993)
Michalski, R.S., Carbonell, J.G., Mitchell, T.M.: Machine Learning-An Artificial Intelligence Approach. Springer, Heidelberg (1983) (printed in Germany)
Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann, San Francisco (1993)
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Wang, S., Wei, J., You, J., Liu, D. (2006). A VPRSM Based Approach for Inducing Decision Trees. In: Wang, GY., Peters, J.F., Skowron, A., Yao, Y. (eds) Rough Sets and Knowledge Technology. RSKT 2006. Lecture Notes in Computer Science(), vol 4062. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11795131_61
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DOI: https://doi.org/10.1007/11795131_61
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-36297-5
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