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Granular Fuzzy Regression Domain Adaptation in Takagi–Sugeno Fuzzy Models
- Source :
- IEEE Transactions on Fuzzy Systems. 26:847-858
- Publication Year :
- 2018
- Publisher :
- Institute of Electrical and Electronics Engineers (IEEE), 2018.
-
Abstract
- In classical data-driven machine learning methods, massive amounts of labeled data are required to build a high-performance prediction model. However, the amount of labeled data in many real-world applications is insufficient, so establishing a prediction model is impossible. Transfer learning has recently emerged as a solution to this problem. It exploits the knowledge accumulated in auxiliary domains to help construct prediction models in a target domain with inadequate training data. Most existing transfer learning methods solve classification tasks; only a few are devoted to regression problems. In addition, the current methods ignore the inherent phenomenon of information granularity in transfer learning. In this study, granular computing techniques are applied to transfer learning. Three granular fuzzy regression domain adaptation methods to determine the estimated values for a regression target are proposed to address three challenging cases in domain adaptation. The proposed granular fuzzy regression domain adaptation methods change the input and/or output space of the source domain's model using space transformation, so that the fuzzy rules are more compatible with the target data. Experiments on synthetic and real-world datasets validate the effectiveness of the proposed methods.
- Subjects :
- Adaptive neuro fuzzy inference system
Fuzzy classification
Neuro-fuzzy
Computer science
business.industry
Applied Mathematics
Granular computing
02 engineering and technology
Machine learning
computer.software_genre
Fuzzy logic
Domain (software engineering)
Data modeling
Computational Theory and Mathematics
Artificial Intelligence
Control and Systems Engineering
020204 information systems
0202 electrical engineering, electronic engineering, information engineering
020201 artificial intelligence & image processing
Artificial intelligence
Transfer of learning
business
computer
Subjects
Details
- ISSN :
- 19410034 and 10636706
- Volume :
- 26
- Database :
- OpenAIRE
- Journal :
- IEEE Transactions on Fuzzy Systems
- Accession number :
- edsair.doi...........64bae88cf6501c1162e2fb975e2cd1ec