@inproceedings{24fc93bf7bcb4462b19f22ab08aec228,
title = "Fuzzy decision tree based on the important degree of fuzzy attribute",
abstract = "There may be many fuzzy attributes in a fuzzy information system. Different fuzzy attribute has different contribution to classification. More important attributes have more contribution than the others to decision-making. In this paper, based on the importance of the fuzzy condition attributes, a new method generating a fuzzy decision tree is proposed, which uses the important degree of the fuzzy condition attribute with respect to the fuzzy decision attributes to select attributes to expand the branches of a fuzzy decision tree. A comparison between the new method and fuzzy ID3 is provided. It is shown that the new method is more efficient than fuzzy ID3.",
keywords = "Condition attribute, Decision attribute, Fuzzy attribute, Fuzzy decision tree, Important degree",
author = "Xi-Zhao WANG and Jun-Hai ZHAI and Su-Fang ZHANG",
note = "This research is supported by the natural science foundation of Hebei Province (F2008000635), by the key project foundation of applied fundamental research of Hebei Province (08963522D), by the plan of 100 excellent innovative scientists of the first group in Education Department of Hebei Province, and by the scientific research foundation of Hebei Province (06213548). ; 7th International Conference on Machine Learning and Cybernetics, ICMLC ; Conference date: 12-07-2008 Through 15-07-2008",
year = "2008",
doi = "10.1109/ICMLC.2008.4620458",
language = "English",
isbn = "9781424420957",
series = "International Conference on Machine Learning and Cybernetics (ICMLC)",
publisher = "IEEE",
pages = "511--516",
booktitle = "Proceedings of the 7th International Conference on Machine Learning and Cybernetics, ICMLC",
}