Unbalanced Data Classification Based on Hesitant Fuzzy Decision Tree

In order to optimize the classification effect of unbalanced data,an improved fuzzy decision tree algorithm is proposed combining the hesitant fuzzy set theory and the decision tree algorithm.The unbalanced data is oversampled by the SMOTE algorithm,the cluster center point of each attribute is obta...

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Bibliographic Details
Published in:Ji suan ji gong cheng Vol. 45; no. 8; pp. 75 - 79,91
Main Author: ZHANG Xu, ZHOU Xinzhi, ZHAO Chengping, SHAO Lun
Format: Journal Article
Language:Chinese
English
Published: Editorial Office of Computer Engineering 01.08.2019
Subjects:
unbalanced data|hesitant fuzzy sets|hesitant fuzzy decision tree(hfdt)|k-means clustering|fuzzy id3 algorithm
ISSN:1000-3428
Online Access:Get full text
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Summary:In order to optimize the classification effect of unbalanced data,an improved fuzzy decision tree algorithm is proposed combining the hesitant fuzzy set theory and the decision tree algorithm.The unbalanced data is oversampled by the SMOTE algorithm,the cluster center point of each attribute is obtained by using the K-means clustering method,and the datasets is fuzzy processed by using two different membership functions.On this basis,the Hesitant Fuzzy Information Gain(HFIG) of each attribute is obtained by the information energy of hesitant fuzzy sets and membership functions.The largest HFIG is used to replace the FIG in the Fuzzy ID3 algorithm as the split criterion of the attribute,and a Hesitant Fuzzy Decision Tree(HFDT) model is constructed for unbalanced data classification.Experimental results show that,compared with traditional classification algorithms such as C4.5,KNN and random forest,the classifier based on HFDT has an average increase of 12.6% on the AUC evaluation index.
ISSN:1000-3428
DOI:10.19678/j.issn.1000-3428.0051759