A general model for fuzzy decision tree and fuzzy random forest

Author:

Zheng Hui1234ORCID,He Jing25ORCID,Zhang Yanchun67,Huang Guangyan8,Zhang Zhenjiang9,Liu Qing10

Affiliation:

1. School of Computer and Control EngineeringUniversity of Chinese Academy of Sciences Beijing China

2. School of Software and Electrical EngineeringSwinburne University of Technology Melbourne Australia

3. College of ComputerNanjing University of Posts and Telecommunications Nanjing China

4. Jiangsu High Technology Research Key Laboratory for Wireless Sensor NetworksNanjing University of Posts and Telecommunications Nanjing China

5. Institute of Information TechnologyNanjing University of Finance and Economics Nanjing China

6. Centre for Applied Informatics, College of Engineering and ScienceVictoria University Melbourne Australia

7. Shanghai Key Research Laboratory of Data ScienceFudan University Shanghai China

8. School of Information Technology, Faculty of Science, Engineering and Built EnvironmentDeakin University Melbourne Australia

9. Institute of Software EngineeringBeijing Jiaotong University Beijing China

10. Software and Computational Systems ProgramData61, Commonwealth Scientific and Industrial Research Organisation (CSIRO) Clayton South Australia

Funder

National Natural Science Foundation of China

Natural Science Foundation of Ningbo

Natural Science Foundation of Zhejiang Province

Guangzhou Science and Technology Program key projects

Publisher

Wiley

Subject

Artificial Intelligence,Computational Mathematics

Reference37 articles.

1. Fuzzy sets

2. On the handling of fuzziness for continuous-valued attributes in decision tree generation

3. Fuzzy Decision Tree Algorithms

4. Constructing optimal binary decision trees is NP-complete

5. UmanolM OkamotoH HatonoI et al.Fuzzy decision trees by fuzzy ID3 algorithm and its application to diagnosis systems. In: Proceedings of 1994 IEEE 3rd International Fuzzy Systems Conference;1994;Orlando FL.

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