@misc{10481/83899,
year = {2023},
month = {5},
url = {https://hdl.handle.net/10481/83899},
abstract = {As a novel architecture of a fuzzy decision tree constructed on fuzzy rules, the fuzzy rule-based
decision tree (FRDT) achieved better performance in terms of both classification accuracy and the
size of the resulted decision tree than other classical decision trees such as C4.5, LADtree, BFtree,
SimpleCart and NBTree. The concept of Z-number extends the classical fuzzy number to model
both uncertain and partial reliable information. Z-numbers have significant potential in rule-based
systems due to their strong representation capability. This paper designs a Z-number-valued rulebased
decision tree (ZRDT) and provides the learning algorithm. Firstly, the information gain is
used to replace the fuzzy confidence in FRDT to select features in each rule. Additionally, we use
the negative samples to generate the second fuzzy numbers that adjust the first fuzzy numbers
and improve the model’s fit to the training data. The proposed ZRDT is compared with the FRDT
with three different parameter values and two classical decision trees, PUBLIC and C4.5, and a
decision tree ensemble method, AdaBoost.NC, in terms of classification effect and size of decision
trees. Based on statistical tests, the proposed ZRDT has the highest classification performance
with the smallest size for the produced decision tree.},
organization = {The project B-TIC-590-UGR20},
organization = {Programa Operativo FEDER 2014-2020},
organization = {Regional Ministry of Economy},
organization = {Knowledge},
organization = {Enterprise and Universities (CECEU) of Andalusia},
organization = {China Scholarship Council (CSC)
(202106070037)},
organization = {Project PID2019-103880RB-I00},
organization = {MCIN/AEI/10.13039/501100011033},
organization = {Andalusian
government through project P20_00673},
publisher = {Elsevier},
keywords = {Z-numbers},
keywords = {Rule-based system},
keywords = {Decision trees},
keywords = {Z-number-valued rule},
keywords = {Classification},
keywords = {Information gain},
title = {Z-number-valued rule-based decision trees},
doi = {10.1016/j.ins.2023.119252},
author = {Li, Yangxue and Herrera Viedma, Enrique and Kou, Gang and Morente Molinera, Juan Antonio},
}