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Abstract: This paper deals with a reinforced cumulative probability distribution approach (CPDA) based method for extracting classification rules. The method includes two phases: (1) automatic generation of the membership function, and (2) use of the corresponding linguistic data to extract classification rules. The proposed method can determine suitable interval boundaries for any given dataset based on its own characteristics, and generate the fuzzy membership functions automatically. Experimental results show that the proposed method surpasses traditional methods in accuracy.

[1]Biswas, R., 1995. An application of fuzzy sets in students’ evaluation. Fuzzy Sets Syst., 74(2):187-194.

[2]Chen, S.M., Lee, S.H., Lee, C.H., 2001. A new method for generating fuzzy rules from numerical data for handling classification problems. Appl. Artif. Intell., 15(7):645-664.

[3]Chou, H.L., Chen, J.S., Cheng, C.H., Teoh, H.J., 2010. Forecasting tourism demand based on improved fuzzy time series model. LNCS, 5990:399-407.

[4]Grzymala-Busse, J.W., 2003. A comparison of three strategies to rule induction from data with numerical attributes. Electron. Notes Theor. Comput. Sci., 82(4):132-140.

[5]Han, J., Kamber, M., 2001. Data Mining: Concepts and Techniques. Morgan Kaufmann Publishers, San Francisco.

[6]Hong, T.P., Lee, C.Y., 1996. Induction of fuzzy rules and membership functions from training examples. Fuzzy Sets Syst., 84(1):33-47.

[7]Huang, P., Zhu, J., 2010. Multi-instance learning for software quality estimation in object-oriented systems: a case study. J. Zhejiang Univ.-Sci. C (Comput. & Electron.), 11(2):130-138.

[8]Jia, P., Dai, J.H., Chen, W.D., Pan, Y.H., Zhu, M.L., 2006. Immune algorithm for discretization of decision systems in rough set theory. J. Zhejiang Univ.-Sci. A, 7(4):602-606.

[9]Law, C.K., 1996. Using fuzzy numbers in educational grading system. Fuzzy Sets Syst., 83(3):311-323.

[10]Li, D.M., 2001. Finite volume method based on the Crouzeix-Raviart element for the Stokes equation. J. Zhejiang Univ.-Sci., 2(2):165-169.

[11]Liu, H., Hussain, F., Tan, C., Dash, M., 2002. Discretization: an enabling technique. Data Min. Knowl. Disc., 6(4):393-423.

[12]Liu, Y.M., Ye, L.B., Zheng, P.Y., Shi, X.R., Hu, B., Liang, J., 2010. Multiscale classification and its application to process monitoring. J. Zhejiang Univ.-Sci. C (Comput. & Electron.), 11(6):425-434.

[13]Newman, D.J., Hettich, S., Blake, C.L., Merz, C.J., 1998. UCI Repository of Machine Learning Databases. Available from http://www.ics.uci.edu/~mlearn/ [Accessed on July 25, 2007].

[14]Quinlan, J.R., 1986. Induction of decision trees. Mach. Learn., 1(1):81-106.

[15]Quinlan, J.R., 1993. C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers, San Mateo, CA.

[16]Rasmani, K.A., Shen, Q., 2006. Data-driven fuzzy rule generation and its application for student academic performance evaluation. Appl. Intell., 25(3):305-319.

[17]Ross, T.J., 2004. Fuzzy Logic with Engineering Applications. John Wiley & Sons, Ltd., USA.

[18]Teoh, H.J., Cheng, C.H., Chu, H.H., Chen, J.S., 2008. Fuzzy time series model based on probabilistic approach and rough set rule induction for empirical research in stock markets. Data Knowl. Eng., 67(1):103-117.

[19]Witten, I.H., Frank, E., 2005. Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann Publishers, San Francisco.

[20]Zadeh, L.A., 1965. Fuzzy sets. Inform. Control, 8(3):338-353.