CD: A Coupled Discretization Algorithm
Abstract
Discretization technique plays an important role in data mining and machine learning. While numeric data is predominant in the real world, many algorithms in supervised learning are restricted to discrete variables. Thus, a variety of research has been conducted on discretization, which is a process of converting the continuous attribute values into limited intervals. Recent work derived from entropy-based discretization methods, which has produced impressive results, introduces information attribute dependency to reduce the uncertainty level of a decision table; but no attention is given to the increment of certainty degree from the aspect of positive domain ratio. This paper proposes a discretization algorithm based on both positive domain and its coupling with information entropy, which not only considers information attribute dependency but also concerns deterministic feature relationship. Substantial experiments on extensive UCI data sets provide evidence that our proposed coupled discretization algorithm generally outperforms other seven existing methods and the positive domain based algorithm proposed in this paper, in terms of simplicity, stability, consistency, and accuracy.
Keywords
Information Entropy Discretization Method Decision Table Discretization Algorithm Decision AttributePreview
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References
- 1.An, A., Cercone, N.: Discretization of Continuous Attributes for Learning Classification Rules. In: Zhong, N., Zhou, L. (eds.) PAKDD 1999. LNCS (LNAI), vol. 1574, pp. 509–514. Springer, Heidelberg (1999)CrossRefGoogle Scholar
- 2.Banda, J.M., Angryk, R.A.: On the effectiveness of fuzzy clustering as a data discretization technique for large-scale classification of solar images. In: FUZZ-IEEE 2009, pp. 2019–2024 (2009)Google Scholar
- 3.Beynon, M.J.: Stability of continuous value discretisation: an application within rough set theory. International Journal of Approximate Reasoning 35, 29–53 (2004)MATHCrossRefGoogle Scholar
- 4.Chen, C., Wang, L.: Rough set-based clustering with refinement using Shannon’s entropy theory. Computers and Mathematics with Applications 52(10-11), 1563–1576 (2006)MathSciNetMATHCrossRefGoogle Scholar
- 5.Chmielewski, M.R., Grzymala-Busse, J.W.: Global discretization of continuous attributes as preprocessing for machine learning. International Journal of Approximate Reasoning 15, 319–331 (1996)MATHCrossRefGoogle Scholar
- 6.Liu, H., Hussain, F., Tan, C.L., Dash, M.: Discretization: an enabling technique. Data Mining and Knowledge Discovery 6, 393–423 (2002)MathSciNetCrossRefGoogle Scholar
- 7.Liu, W., Chawla, S.: Class Confidence Weighted kNN Algorithms for Imbalanced Data Sets. In: Huang, J.Z., Cao, L., Srivastava, J. (eds.) PAKDD 2011, Part II. LNCS, vol. 6635, pp. 345–356. Springer, Heidelberg (2011)CrossRefGoogle Scholar
- 8.Pawlak, Z., Wong, S.K.M., Ziarko, W.: Rough sets: probabilistic versus deterministic approach. International Journal of Man-Machine Studies 29, 81–95 (1988)MATHCrossRefGoogle Scholar
- 9.Qin, B., Xia, Y., Li, F.: DTU: A Decision Tree for Uncertain Data. In: Theeramunkong, T., Kijsirikul, B., Cercone, N., Ho, T.-B. (eds.) PAKDD 2009. LNCS, vol. 5476, pp. 4–15. Springer, Heidelberg (2009)CrossRefGoogle Scholar
- 10.Son, N.H., Szczuka, M.: Rough sets in KDD. In: PAKDD 2005, pp. 1–91 (2005)Google Scholar
- 11.Wang, C., Cao, L., Wang, M., Li, J., Wei, W., Ou, Y.: Coupled nominal similarity in unsupervised learning. In: CIKM 2011, pp. 973–978 (2011)Google Scholar
- 12.Wang, G., Zhao, J., An, J., Wu, Y.: A comparative study of algebra viewpoint and information viewpoint in attribute reduction. Fundamenta Informaticae 68, 289–301 (2005)MathSciNetMATHGoogle Scholar
- 13.Yang, Y., Webb, G.I.: Discretization for Naive-Bayes learning: managing discretization bias and variance. Machine Learning 74, 39–74 (2009)CrossRefGoogle Scholar
- 14.Zhang, X., Wu, J., Yang, X., Lu, T.: Estimation of market share by using discretization technology: an application in China mobile. In: ICCS 2008, pp. 466–475 (2008)Google Scholar