Paper Info
Title
A general model for fuzzy decision tree and fuzzy random forest.
Abstract
The problem of risk classification and prediction, an essential research direction, aiming to identify and predict risks for various applications, has been researched in this paper. To identify and predict risks, numerous researchers build models on discovering hidden information of a label (positive credit or negative credit). Fuzzy logic is robust in dealing with ambiguous data and, thus, benefits the problem of classification and prediction. However, the way to apply fuzzy logic optimally depends on the characteristics of the data and the objectives, and it is extraordinarily tricky to find such a way. This paper, therefore, proposes a general membership function model for fuzzy sets (GMFMFS) in the fuzzy decision tree and extend it to the fuzzy random forest method. The proposed methods can be applied to identify and predict the credit risks with almost optimal fuzzy sets. In addition, we analyze the feasibility of our GMFMFS and prove our GMFMFS-based linear membership function can be extended to a nonlinear membership function without a significant increase in computing complex. Our GMFMFS-based fuzzy decision tree is tested with a real dataset of US credit, Susy dataset of UCI, and synthetic datasets of big data. The results of experiments further demonstrate the effectiveness and potential of our GMFMFS-based fuzzy decision tree with linear membership function and nonlinear membership function.
Year
DOI
Venue
2019
10.1111/coin.12195
COMPUTATIONAL INTELLIGENCE
Keywords
DocType
Volume
fuzzy decision tree,fuzzy random forest,membership function,risk classification and prediction
Journal
35.0
Issue
ISSN
Citations 
2.0
0824-7935
1
PageRank 
References 
Authors
0.34
0
6
Name
Order
Citations
PageRank
Hui Zheng17315.94
Jing He254.44
Yanchun Zhang33059284.90
Guangyan Huang441942.85
Zhen-Jiang Zhang530634.31
Qing Liu642151.42