The Community for Technology Leaders
RSS Icon
Subscribe
Issue No.08 - Aug. (2012 vol.24)
pp: 1435-1447
Khurram Shehzad , University of Engineering and Technology, Taxila
ABSTRACT
Abstract—Discretization is a critical component of data mining whereby continuous attributes of a data set are converted into discrete ones by creating intervals either before or during learning. There are many good reasons for preprocessing discretization, such as increased learning efficiency and classification accuracy, comprehensibility of data mining results, as well as the inherent limitation of a great majority of learning algorithms to handle only discrete data. Many preprocessing discretization techniques have been proposed to date, of which the Entropy-MDLP discretization has been accepted as by far the most effective in the context of both decision tree learning and rule induction algorithms. This paper presents a new discretization technique EDISC which utilizes the entropy-based principle but takes a class-tailored approach to discretization. The technique is applicable in general to any covering algorithm, including those that use the class-per-class rule induction methodology such as CN2 as well as those that use a seed example during the learning phase, such as the RULES family. Experimental evaluation has proved the efficiency and effectiveness of the technique as a preprocessing discretization procedure for CN2 as well as RULES-7, the latest algorithm among the RULES family of inductive learning algorithms.
INDEX TERMS
Discretization, continuous values, discrete values, data transformation, data mining, machine learning, inductive learning, supervised learning, rule induction.
CITATION
Khurram Shehzad, "EDISC: A Class-Tailored Discretization Technique for Rule-Based Classification", IEEE Transactions on Knowledge & Data Engineering, vol.24, no. 8, pp. 1435-1447, Aug. 2012, doi:10.1109/TKDE.2011.101
REFERENCES
[1] A. An and N. Cercone, "Discretization of Continuous Attributes for Learning Classification Rules," Proc. Third Pacific-Asia Conf. Methodologies for Knowledge Discovery and Data Mining, pp. 509-514, 1999.
[2] U.M. Fayyad and K.B. Irani, "Multi-Interval Discretization of Continuous-Valued Attributes for Classification Learning," Proc. 13th Int'l Joint Conf. Artificial Intelligence, pp. 1022-1027, 1993.
[3] R. Kerber, "ChiMerge: Discretization of Numeric Attributes," Proc. 10th Nat'l Conf. Artificial Intelligence, pp. 123-128, 1992.
[4] K.M. Ho and P.D. Scott, "Zeta: A Global Method for Discretization of Continuous Variables," Proc. Third Int'l Conf. Knowledge Discovery and Data Mining (KDD '97), pp. 191-194, 1997.
[5] A.K.C. Wong and D.K.Y. Chiu, "Synthesizing Statistical Knowledge from Incomplete Mixed-Mode Data," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. PAMI-9, no. 6, pp. 796-805, Nov. 1987.
[6] L. Breiman, J. Friedman, C.J. Stone, and R.A. Olshen, Classification and Regression Trees. Wadsworth, 1984.
[7] P. Clark and T. Niblett, "The CN2 Induction Algorithm," Machine Learning, vol. 3, pp. 261-283, 1989.
[8] J.R. Quinlan, "Learning Efficient Classification Procedures and Their Application to Chess End Games," Machine Learning: An Artificial Intelligence Approach, S.R. Michalski, G.J. Carbonell, and M.T. Mitchell, eds., vol. I, pp. 463-482, Tioga Publishing Co., 1983.
[9] J.R. Quinlan, "Induction of Decision Trees," Machine Learning, vol. 1, no. 1, pp. 81-106, 1986.
[10] J.R. Quinlan, "Learning Logical Definitions from Relations," Machine Learning, vol. 5, no. 3, pp. 239-266, 1990.
[11] I.H. Witten and E. Frank, Data Mining—Practical Machine Learning Tools and Techniques. Morgan Kaufmann Publishers, 2005.
[12] A. Mittal and L.F. Cheong, "Employing Discrete Bayes Error Rate for Discretization and Feature Selection," Proc. IEEE Int'l Conf. Data Mining, pp. 298-305, 2002.
[13] A.A. Afify, "Design and Analysis of Scalable Rule Induction Systems," PhD thesis, Systems Eng. Division, Univ. of Wales, Cardiff, UK, 2004.
[14] H. Liu, F. Hussain, C.L. Tan, and M. Dash, "Discretization: An Enabling Technique," Data Mining and Knowledge Discovery, vol. 6, no. 4, pp. 393-423, 2002.
[15] F. Muhlenbach and R. Rakotomalala, "Discretization of Continuous Attributes," Encyclopedia of Data Warehousing and Mining, E. Brennan, A. Bubnis, R. Davies, and S. VanderHook, eds., vol. I, pp. 397-402, Idea Group Publishing, 2005.
[16] D.T. Pham and M.S. Aksoy, "RULES: A Simple Rule Extraction System," Expert Systems with Applications, vol. 8, no. 1, pp. 59-65, 1995.
[17] R.S. Michalski, "On the Quasi-Minimal Solution of the General Covering Problem," Proc. Fifth Int'l Symp. Information Processing, pp. 125-128, 1969.
[18] D.T. Pham and A.A. Afify, "RULES-6: A Simple Rule Induction Algorithm for Supporting Decision Making," Proc. 31st Ann. Conf. IEEE Industrial Electronics Soc. (IECON), pp. 2184-2189, 2005.
[19] D.T. Pham and S.S. Dimov, "An Efficient Algorithm for Automatic Knowledge Acquisition," Pattern Recognition, vol. 30, no. 7, pp. 1137-1143, 1997.
[20] D.T. Pham and A.A. Afify, "SRI: A Scalable Rule Induction Algorithm," Proc. Inst. of Mechanical Eng. Part C: J. Mechanical Eng. Science, vol. 220, no. 4, pp. 537-552, 2006.
[21] U.M. Fayyad and K.B. Irani, "Multi-Interval Discretization of Continuous-Valued Attributes for Classification Learning," Proc. 13th Int'l Joint Conf. Artificial Intelligence, pp. 1022-1027, 1993.
[22] D.T. Pham and A.A. Afify, "Online Discretization of Continuous-Valued Attributes in Rule Induction," Proc. Inst. of Mechanical Engineers, Part C: J. Mechanical Eng. Science, vol. 219, no. 8, pp. 829-842, 2005.
[23] P. Clark and R. Boswell, "Rule Induction with CN2: Some Recent Improvements," Proc. Fifth European Working Session on Learning, pp. 151-163, 1991.
[24] J. Dougherty, R. Kohavi, and M. Sahami, "Supervised and Unsupervised Discretization of Continuous Features," Proc. 12th Int'l Conf. Machine Learning, pp. 194-202, 1995.
[25] S. Kotsiantis and D. Kanellopoulos, "Discretization Techniques: A Recent Survey," GESTS Int'l Trans. Computer Science and Eng., vol. 32, no. 1, pp. 47-58, 2006.
[26] Y. Yang and G.I. Webb, "Discretization for Data Mining," Encyclopedia of Data Warehousing and Mining, E. Brennan, A. Bubnis, R. Davies, and S. VanderHook, eds., vol. I, pp. 397-402, Idea Group Publishing, 2005.
[27] R.C. Holte, "Very Simple Classification Rules Perform Well on Most Commonly Used Datasets," Machine Learning, vol. 11, no. 1, pp. 63-90, 1993.
[28] S.D. Bay, "Multivariate Discretization of Continuous Variables for Set Mining," Proc. Sixth ACM SIGKDD Int'l Conf. Knowledge Discovery and Data Mining, pp. 315-319, 2000.
[29] J. Gama, L. Torgo, and C. Soares, "Dynamic Discretization of Continuous Attributes," Proc. Sixth Ibero-Am. Conf. AI: Progress in Artificial Intelligence, pp. 160-169, 1998.
[30] J.R. Quinlan, C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers, Inc., 1993.
[31] M. Richeldi and M. Rossotto, "Class-Driven Statistical Discretization of Continuous Attributes," Proc. Eighth European Conf. Machine Learning, pp. 335-338, 1995.
[32] H. Ishibuchi and T. Yamamoto, "Deriving Fuzzy Discretization from Interval Discretization," Proc. 12th IEEE Int'l Conf. Fuzzy Systems, pp. 749-754, 2003.
[33] C.E. Shannon and W. Weaver, A Mathematical Theory of Communication. Univ. of Illinois Press, 1963.
[34] C.J. Thornton, Techniques in Computational Learning. Chapman and Hall Computing, 1992.
[35] B. Cestnik, "Estimating Probabilities: A Crucial Task in Machine Learning," Proc. European Conf. Artificial Intelligence, pp. 147-149, 1990.
[36] C.-J. Tsai, C.-I. Lee, and W.-P. A Yang, "Discretization Algorithm Based on Class-Attribute Contingency Coefficient," Information Sciences, vol. 178, no. 3, pp. 714-731, 2008.
[37] L. Kurgan and K. Cios, "CAIM Discretization Algorithm," IEEE Trans. Knowledge and Data Eng., vol. 16, no. 2, pp. 145-153, Feb. 2004.
[38] Y. Yang and G. Webb, "Discretization for Naive-Bayes Learning: Managing Discretization Bias and Variance," Machine Learning, vol. 74, no. 1, pp. 39-74, 2009.
[39] C.L. Blake and C.J. Merz, "UCI Repository of Machine Learning Databases," Univ. of California, Dept. of Information and Computer Science, Irvine, CA, http://archive.ics.uci.eduml/, 1998.
[40] R.D. Veaux, "Datasets for Use in the Data Mining Course," Williams College, Williamstown, Dept. of Math. and Statistics, Bronfman Science Center, MA, 01267, USA, http://www. williams.edu/Mathematics/rdeveaux data.html, 2007.
[41] R. Kohavi, "A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection," Proc. 14th Int'l Joint Conf. Artificial Intelligence, pp. 1137-1143, 1995.
[42] I. Kuwajima, Y. Nojima, and H. Ishibuchi, "Effects of Constructing Fuzzy Discretization from Crisp Discretization for Rule-Based Classifiers," Artificial Life and Robotics, vol. 13, no. 1, pp. 294-297, 2008.
[43] R.G. Mehta, D.P. Rana, and M.A. Zaveri, "A Novel Fuzzy Based Classification for Data Mining Using Fuzzy Discretization," Proc. WRI World Congress on Computer Science and Information Eng., pp. 713-717, 2009.
17 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool