Fuzzy Regression Transfer Learning in Takagi-Sugeno Fuzzy Models

Data science is a research field concerned with processes and systems that extract knowledge from massive amounts of data. In some situations, however, data shortage renders existing data-driven methods difficult or even impossible to apply. Transfer learning has recently emerged as a way of exploit...

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Bibliographic Details
Published in:IEEE transactions on fuzzy systems 2017-12, Vol.25 (6), p.1795-1807
Main Authors: Hua Zuo, Guangquan Zhang, Pedrycz, Witold, Behbood, Vahid, Jie Lu
Format: Article
Language:English
Subjects:
Data mining
Data models
Fuzzy model
fuzzy rules
Input variables
Knowledge acquisition
Knowledge management
Learning systems
Machine learning
Performance enhancement
Prototypes
regression
Regression models
Takagi-Sugeno model
transfer learning
Uncertainty
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Summary:Data science is a research field concerned with processes and systems that extract knowledge from massive amounts of data. In some situations, however, data shortage renders existing data-driven methods difficult or even impossible to apply. Transfer learning has recently emerged as a way of exploiting previously acquired knowledge to solve new yet similar problems much more quickly and effectively. In contrast to classical data-driven machine learning methods, transfer learning methods exploit the knowledge accumulated from data in auxiliary domains to facilitate predictive modeling in the current domain. A significant number of transfer learning methods that address classification tasks have been proposed, but studies on transfer learning in the case of regression problems are still scarce. This study focuses on using transfer learning techniques to handle regression problems in a domain that has insufficient training data. We propose an original fuzzy regression transfer learning method, based on fuzzy rules, to address the problem of estimating the value of the target for regression. A Takagi-Sugeno fuzzy regression model is developed to transfer knowledge from a source domain to a target domain. Experimental results using synthetic data and real-world datasets demonstrate that the proposed fuzzy regression transfer learning method significantly improves the performance of existing models when tackling regression problems in the target domain.
ISSN:1063-6706
1941-0034
DOI:10.1109/TFUZZ.2016.2633376