Decision tree classifiers for evidential attribute values and class labels

被引：44

作者：

Trabelsi, Asma ^{[1
,2
]}

Elouedi, Zied ^{[1
]}

Lefevre, Eric ^{[2
]}

机构：

[1] Univ Tunis, Inst Super Gest Tunis, LARODEC, Tunis, Tunisia

[2] Univ Artois, EA 3926, Lab Genie Informat & Automat Artois LGI2A, F-62400 Bethune, France

来源：

FUZZY SETS AND SYSTEMS | 2019年 / 366卷

关键词：

Decision tree classifier; Imperfect data; Evidence theory; TRANSFERABLE BELIEF MODEL; COMBINATION; ASSOCIATION;

D O I：

10.1016/j.fss.2018.11.006

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

Decision trees are well-known machine learning techniques for solving complex classification problems. Despite their great success, the standard decision tree algorithms do not have the ability to process imperfect knowledge, meaning uncertain, imprecise and incomplete data. In this paper, we develop new decision tree approaches to cope with data that have uncertain attribute values and class labels. More concretely, we tackle the case where the uncertainty is represented and managed through the evidence theory. (C) 2018 Published by Elsevier B.V.

引用

页码：46 / 62

页数：17

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