Abstract
Compared with conventional decision tree techniques, fuzzy decision tree (FDT) inductive learning algorithms are more powerful and practical to handle with ambiguities in classification problems. The resultant rules from FDTs can be used in decision-making with similar nature of our human beings by inference mechanism. A parameter, namely significant level (SL) ,α plays an important role in the entire process of building FDTs. It greatly affects the computation of fuzzy entropy and classification results of FDTs, however this important parameter value is usually estimated based on users by domain knowledge, personal experience and requirements. As a result of this, it will be hard to build a high performance FDT without an optimal SL option in practice. This paper aims at developing a method to determine an optimal SL value through analyzing the relationship between the fuzzy entropy and α. The main contribution of this work is to provide some guidelines for selecting the SL parameter α in order to build FDTs with better classification performance. Six data sets from the UCI Machine Learning database are employed in the study. Experimental results and discussions are given.
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© 2003 Springer-Verlag Berlin Heidelberg
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Wang, X., Zhao, M., Wang, D. (2003). Selection of Parameters in Building Fuzzy Decision Trees. In: Gedeon, T.(.D., Fung, L.C.C. (eds) AI 2003: Advances in Artificial Intelligence. AI 2003. Lecture Notes in Computer Science(), vol 2903. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24581-0_24
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DOI: https://doi.org/10.1007/978-3-540-24581-0_24
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