Transfer learning in Takagi-Sugeno fuzzy models
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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 way of exploiting previously acquired knowledge to solve new yet similar problems much more efficiently and effectively. It exploits the knowledge accumulated in auxiliary domains to help construct prediction models in a target domain with inadequate training data. Most existing methods on transfer learning address classification tasks, but studies on transfer learning in the case of regression problems, which is an important category in prediction models, are still scarce. In addition, the existing works ignore the inherent phenomenon of uncertainty - a crucial factor during the knowledge transfer process. To fill these gaps, this research develops algorithms and methods to deal with transfer learning in homogeneous and heterogeneous spaces using fuzzy rule-based models. Fuzzy rules are first generated from the source domain through a learning process. These rules, as acquired knowledge, are then transferred to the target domain. First, a novel fuzzy rule-based domain adaptation method is proposed to transfer knowledge between domains in homogeneous spaces. It utilizes the existing fuzzy rules of source domain and modifies the input space with nonlinear mappings to adapt to the current regression tasks in the target domain. Second, a granular fuzzy domain adaptation framework, comprising three methods, is developed to handle the knowledge transfer problems based on the idea of granular computing. Thirdly, a fuzzy domain adaptation method is developed to handle the case that the numbers of fuzzy rules in two domains do not match. In addition, the proposed methods are also used to solve the classification problems in transfer learning. Fourthly, an innovative method that combines an infinite Gaussian mixture model with active learning is presented to discover the structure of data and actively augment information in a target domain. Fifthly, a fuzzy heterogeneous domain adaptation method is proposed to transfer knowledge in heterogeneous spaces. The proposed algorithms and methods are validated in each step of development using experiments performed on both synthetic and real-world datasets. The results show that this study significantly improve the performance of existing models when solving new tasks in the target domain in both homogeneous and heterogeneous domain adaptation settings.
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