Increasing Accuracy and Explainability in Fuzzy Regression Trees: An Experimental Analysis-Reference-Cited by-同舟云学术

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Increasing Accuracy and Explainability in Fuzzy Regression Trees: An Experimental Analysis

Published:2022-07-18 Issue: Volume: Page:
ISSN:
Container-title:2022 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)
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Author:

Bechini Alessio¹,Corcuera Barcena Jose Luis¹,Ducange Pietro¹,Marcelloni Francesco¹,Renda Alessandro¹

Affiliation:

1. University of Pisa,Department of Information Engineering,Pisa,Italy,56122

Funder

European Commission

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9882530/9882539/09882604.pdf?arnumber=9882604

Reference22 articles.

1. Decision Trees

2. Learning with continuous classes;quinlan;5th Australian Joint Conference on Artificial Intelligence,1992

3. A Survey of Fuzzy Decision Tree Classifier

4. XAI Models for Quality of Experience Prediction in Wireless Networks

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2. Automated Aviation Wind Nowcasting: Exploring Feature-Based Machine Learning Methods;Applied Sciences;2023-09-12

3. Interpretable controlled object model of furnace temperature for MSWI process based on a novle linear regression decision tree;2023 35th Chinese Control and Decision Conference (CCDC);2023-05-20

4. Takagi–Sugeno Fuzzy Regression Trees with Application to Complex Industrial Modeling;IEEE Transactions on Fuzzy Systems;2022

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