Paper Info
Title
Enhanced Knowledge-Leverage-Based TSK Fuzzy System Modeling for Inductive Transfer Learning.
Abstract
The knowledge-leverage-based Takagi--Sugeno--Kang fuzzy system (KL-TSK-FS) modeling method has shown promising performance for fuzzy modeling tasks where transfer learning is required. However, the knowledge-leverage mechanism of the KL-TSK-FS can be further improved. This is because available training data in the target domain are not utilized for the learning of antecedents and the knowledge transfer mechanism from a source domain to the target domain is still too simple for the learning of consequents when a Takagi--Sugeno--Kang fuzzy system (TSK-FS) model is trained in the target domain. The proposed method, that is, the enhanced KL-TSK-FS (EKL-TSK-FS), has two knowledge-leverage strategies for enhancing the parameter learning of the TSK-FS model for the target domain using available information from the source domain. One strategy is used for the learning of antecedent parameters, while the other is for consequent parameters. It is demonstrated that the proposed EKL-TSK-FS has higher transfer learning abilities than the KL-TSK-FS. In addition, the EKL-TSK-FS has been further extended for the scene of the multisource domain.
Year
DOI
Venue
2016
10.1145/2903725
ACM TIST
Keywords
Field
DocType
Enhanced KL-TSK-FS,fuzzy systems,knowledge leverage,missing data,fuzzy modeling,transfer learning
Data mining,Leverage (finance),Multi-task learning,Inductive transfer,Computer science,Transfer of learning,Fuzzy logic,Knowledge transfer,Artificial intelligence,Missing data,Fuzzy control system,Machine learning
Journal
Volume
Issue
ISSN
8
1
2157-6904
Citations 
PageRank 
References 
7
0.49
39
Authors
5
Name
Order
Citations
PageRank
Zhaohong Deng164735.34
Yizhang Jiang238227.24
Hisao Ishibuchi37385503.41
Kup-Sze Choi452647.41
Shitong Wang51485109.13