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Optimized Fuzzy Decision Tree Data Mining for Engineering Applications

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Advances in Data Mining. Applications and Theoretical Aspects (ICDM 2011)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 6870))

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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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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

  • Print ISBN: 978-3-642-23183-4

  • Online ISBN: 978-3-642-23184-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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