research-article

A fuzzy decision tree approach based on data distribution construction

Authors:

Yong ShiAuthors Info & Claims

ACSW '17: Proceedings of the Australasian Computer Science Week Multiconference

Article No.: 5, Pages 1 - 10

https://doi.org/10.1145/3014812.3014817

Published: 31 January 2017 Publication History

Abstract

In the credit risk evaluation process, credit classification is one of the most popular method to assess the according risk. In real world application, credit classification can distinguish the bad credit transactions from the good ones, which avoids potential risks. To identify the potential risks (bad credit), we employ a fuzzy decision tree method to accomplish the classification process for the credit risk evaluation, because it owns the advantages of ease of interpretation, reduction of information loss and competitive theoretical basis. To further extend these advantages, this paper proposes a novel data-distribution based fuzzy decision tree (DDBFDT) approach, which not only represents a new fuzzy partition points finding algorithm but also formulates the process of building membership function. The merits of our approach is fourfold: 1) a new error-related metric to select the best attribute for creating our fuzzy decision tree; 2) a data-distribution aware algorithm in the process of partition point searching 3) development of less computing complex non-linear membership function and more interpretable fuzzy sets building strategy with original data distribution involved; 4) better performance than similar models and high readability due to our fuzzy result of classification, as well as robustness and resisting disturbance. Our proposed DDBFDT approach is testified by both rigorous theoretics and a couple of experiments using public opened data-sets as well as synthesized data-sets.

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Cited By

Chen JLi PFang WZhou NYin YZheng HXu HWang R(2021)Fuzzy Frequent Pattern Mining Algorithm Based on Weighted Sliding Window and Type-2 Fuzzy Sets over Medical Data StreamWireless Communications & Mobile Computing10.1155/2021/66622542021Online publication date: 1-Jan-2021
https://dl.acm.org/doi/10.1155/2021/6662254
Chen JZheng HLi PZhang ZLi HLiu W(2020)Fuzzy Association Rule Mining Algorithm Based on Load ClassifierData Science10.1007/978-981-15-2810-1_18(178-191)Online publication date: 2-Feb-2020
https://doi.org/10.1007/978-981-15-2810-1_18
Zheng HHe JZhang YHuang GZhang ZLiu Q(2018)A general model for fuzzy decision tree and fuzzy random forestComputational Intelligence10.1111/coin.1219535:2(310-335)Online publication date: 23-Nov-2018
https://doi.org/10.1111/coin.12195
Show More Cited By

A fuzzy decision tree approach based on data distribution construction
1. Computing methodologies
  1. Artificial intelligence
    1. Knowledge representation and reasoning
  2. Machine learning
    1. Learning paradigms
      1. Supervised learning

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cover image ACM Other conferences

ACSW '17: Proceedings of the Australasian Computer Science Week Multiconference

January 2017

615 pages

ISBN:9781450347686

DOI:10.1145/3014812

Copyright © 2017 ACM.

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Publication History

Published: 31 January 2017

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National Natural Science Foundation of China

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ACSW 2017

ACSW 2017: Australasian Computer Science Week 2017

January 30 - February 3, 2017

Geelong, Australia

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ACSW '17 Paper Acceptance Rate 78 of 156 submissions, 50%;

Overall Acceptance Rate 204 of 424 submissions, 48%

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Cited By

Chen JLi PFang WZhou NYin YZheng HXu HWang R(2021)Fuzzy Frequent Pattern Mining Algorithm Based on Weighted Sliding Window and Type-2 Fuzzy Sets over Medical Data StreamWireless Communications & Mobile Computing10.1155/2021/66622542021Online publication date: 1-Jan-2021
https://dl.acm.org/doi/10.1155/2021/6662254
Chen JZheng HLi PZhang ZLi HLiu W(2020)Fuzzy Association Rule Mining Algorithm Based on Load ClassifierData Science10.1007/978-981-15-2810-1_18(178-191)Online publication date: 2-Feb-2020
https://doi.org/10.1007/978-981-15-2810-1_18
Zheng HHe JZhang YHuang GZhang ZLiu Q(2018)A general model for fuzzy decision tree and fuzzy random forestComputational Intelligence10.1111/coin.1219535:2(310-335)Online publication date: 23-Nov-2018
https://doi.org/10.1111/coin.12195
Yu HLu JZhang G(2017)Learning a fuzzy decision tree from uncertain data2017 12th International Conference on Intelligent Systems and Knowledge Engineering (ISKE)10.1109/ISKE.2017.8258728(1-7)Online publication date: Nov-2017
https://doi.org/10.1109/ISKE.2017.8258728

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