Abstract
In this chapter, label semantics theory is applied to designing transparent data mining models. A label semantics based decision tree model is proposed where nodes are linguistic descriptions of variables and leaves are sets of appropriate labels. For each branch, instead of labeling it with a certain class, the probability of a particular class given this branch can be computed based on the given training dataset. This new model is referred to as a linguistic decision tree (LDT).
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© 2014 Zhejiang University Press, Hangzhou and Springer-Verlag Berlin Heidelberg
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Qin, Z., Tang, Y. (2014). Linguistic Decision Trees for Classification. In: Uncertainty Modeling for Data Mining. Advanced Topics in Science and Technology in China. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41251-6_4
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DOI: https://doi.org/10.1007/978-3-642-41251-6_4
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