@inbook{4c1a282f894942eb9a4e6d621665c1b6,
title = "Linguistic Decision Trees for Classification",
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).",
keywords = "Focal Element, Information Granule, Label Semantic, Merging Algorithm, Threshold Probability",
author = "Zengchang Qin and Yongchuan Tang",
note = "Publisher Copyright: {\textcopyright} 2014, Zhejiang University Press, Hangzhou and Springer-Verlag Berlin Heidelberg.",
year = "2014",
doi = "10.1007/978-3-642-41251-6\_4",
language = "英语",
series = "Advanced Topics in Science and Technology in China",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "77--119",
booktitle = "Advanced Topics in Science and Technology in China",
address = "德国",
}