Summary
Decision tree construction is a popular approach in data mining and machine learning, and some variants of decision tree algorithms have been proposed to deal with different types of data. In this paper, we present a bipolar interpretation of fuzzy decision trees. With the interpretation, various types of decision trees can be represented in a unified form. The edges of a fuzzy decision tree are labeled by fuzzy decision logic formulas and the nodes are split according to the satisfaction of these formulas in the data records. We present a construction algorithm for general fuzzy decision trees and show its application to different types of training data.
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Fan, TF., Liau, CJ., Liu, DR. (2008). A Bipolar Interpretation of Fuzzy Decision Trees. In: Lin, T.Y., Xie, Y., Wasilewska, A., Liau, CJ. (eds) Data Mining: Foundations and Practice. Studies in Computational Intelligence, vol 118. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78488-3_6
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DOI: https://doi.org/10.1007/978-3-540-78488-3_6
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