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A general model for fuzzy decision tree and fuzzy random forest

journal contribution
posted on 2019-05-01, 00:00 authored by H Zheng, J He, Y Zhang, Guangyan HuangGuangyan Huang, Z Zhang, Q Liu
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.

History

Journal

Computational Intelligence

Volume

35

Pagination

310-335

Location

Chichester, Eng.

ISSN

0824-7935

eISSN

1467-8640

Language

English

Notes

Early View Article

Publication classification

C1 Refereed article in a scholarly journal

Copyright notice

2018, Wiley Periodicals, Inc.

Issue

2

Publisher

WILEY