Journal
IEEE TRANSACTIONS ON FUZZY SYSTEMS
Volume 28, Issue 9, Pages 1996-2008Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TFUZZ.2019.2930022
Keywords
Fuzzy systems; Training; Data models; Machine learning; Computational modeling; Training data; Two dimensional displays; Ensemble learning; fuzzy system; patch learning (PL); regression
Funding
- National Natural Science Foundation of China [61873321]
- 111 Project on Computational Intelligence and Intelligent Control [B18024]
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There have been different strategies to improve the performance of a machine learning model, e.g., increasing the depth, width, and/or nonlinearity of the model, and using ensemble learning to aggregate multiple base/weak learners in parallel or in series. This article proposes a novel strategy called patch learning (PL) for this problem. It consists of three steps: first, train an initial global model using all training data; second, identify from the initial global model the patches that contribute the most to the learning error, and train a (local) patch model for each such patch; and, third, update the global model using training data that do not fall into any patch. To use a PL model, we first determine if the input falls into any patch. If yes, then the corresponding patch model is used to compute the output. Otherwise, the global model is used. We explain in detail how PL can be implemented using fuzzy systems. Five regression problems on one-dimensional (1-D)/2-D/3-D curve fitting, nonlinear system identification, and chaotic time-series prediction, verified its effectiveness. To our knowledge, the PL idea has not appeared in the literature before, and it opens up a promising new line of research in machine learning.
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