Abstract
Manufacturing organizations are striving to remain competitive in an era of increased competition and every-changing conditions. Manufacturing technology selection is a key factor in the growth of an organization and a fundamental challenge is effectively managing the computation of data to support future decision-making. Classification is a data mining technique used to predict group membership for data instances. Popular methods include decision trees and neural networks. This paper investigates a unique fuzzy reasoning method suited to engineering applications using fuzzy decision trees.
The paper focuses on the inference stages of fuzzy decision trees to support decision-engineering tasks. The relaxation of crisp decision tree boundaries through fuzzy principles increases the importance of the degree of confidence exhibited by the inference mechanism. Industrial philosophies have a strong influence on decision practices and such strategic views must be considered. The paper is organized as follows: introduction to the research area, literature review, proposed inference mechanism and numerical example. The research is concluded and future work discussed.
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References
Rao, V.: Decision making in the manufacturing environment using an improved PROMETHEE method. International Journal of Production Research 48(16), 4665–4682 (2010)
Chang, P.C., Fan, C.Y., Dzan, W.Y.: A CBR-based fuzzy decision tree approach for database classification. Expert Systems with Applications 37(1), 214–225 (2010)
Abu-halaweh, N.M.: Integrating Information Theory Measures and a Novel Rule-Set-Reduction Technique to Improve Fuzzy Decision Tree Induction Algorithms, Deptartment of Computer Science. Georgia State University, Atlanta (2009)
Anand, S.S., Buchner, A.G.: Decision support using data mining. Financial Times Management, London (1998)
Janikow, C.: Fuzzy decision trees: issues and methods. IEEE Transactions on Systems, Man, and Cybernetics, Part B 28(1), 1 (1998)
Quinlan, J.: Decision trees and decision-making. IEEE Transactions on Systems, Man and Cybernetics 20(2), 339–346 (1990)
Olaru, C., Wehenkel, L.: A complete fuzzy decision tree technique. Fuzzy Sets and Systems 138(2), 221–254 (2003)
Crockett, K.A.: Fuzzy Rule Induction from Data Domains, Department of Computing and Mathematics, p. 219. Manchester Metropolitan University, Manchester (1998)
Harding, J., Shahbaz, M., Kusiak, A.: Data mining in manufacturing: a review. Journal of Manufacturing Science and Engineering 128, 969 (2006)
Lee, M.: The knowledge-based factory. Artificial Intelligence in Engineering 8(2), 109–125 (1993)
Irani, K.B., et al.: Applying machine learning to semiconductor manufacturing. IEEE Expert Intelligent Systems and their Applications, 41–47 (1993)
Piatetsky-Shapiro, G.: The data-mining industry coming of age. IEEE Intelligent Systems and their Applications 14(6), 32–34 (1999)
Wang, T.C., Lee, H.D.: Constructing a fuzzy decision tree by integrating fuzzy sets and entropy. WSEAS Transactions on Information Science and Applications 3(8), 1547–1552 (2006)
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Evans, L., Lohse, N. (2011). Optimized Fuzzy Decision Tree Data Mining for Engineering Applications. In: Perner, P. (eds) Advances in Data Mining. Applications and Theoretical Aspects. ICDM 2011. Lecture Notes in Computer Science(), vol 6870. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23184-1_18
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DOI: https://doi.org/10.1007/978-3-642-23184-1_18
Publisher Name: Springer, Berlin, Heidelberg
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