Abstract
In this paper we present a way of constructing and using fuzzy decision trees for machine learning from sequential and incomplete data. We develop a theory and technique for processing such data. At first, we introduce a decision tree with fuzzy attributes and class the properties of fuzzy attributes and classes, where we will introduce the fuzzy division of objects in classes in a different way than that used in the literature. Then we will also introduce properties of fuzzy attribute value to enrich the fuzziness of our decision trees.
The original version of this chapter was revised: The copyright line was incorrect. This has been corrected. The Erratum to this chapter is available at DOI: 10.1007/978-0-387-35602-0_35
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© 2002 IFIP International Federation for Information Processing
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Chen, G., Lu, R., Jin, Z. (2002). Learning Fuzzy Decision Trees from Sequential and Incomplete Data. In: Musen, M.A., Neumann, B., Studer, R. (eds) Intelligent Information Processing. IIP 2002. IFIP — The International Federation for Information Processing, vol 93. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-35602-0_24
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DOI: https://doi.org/10.1007/978-0-387-35602-0_24
Publisher Name: Springer, Boston, MA
Print ISBN: 978-1-4757-1031-1
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