Can supervise: YES
Zuo, H, Lu, J, Zhang, G & Liu, F 2019, 'Fuzzy Transfer Learning Using an Infinite Gaussian Mixture Model and Active Learning', IEEE Transactions on Fuzzy Systems, vol. 27, no. 2, pp. 291-303.View/Download from: UTS OPUS or Publisher's site
© 2018 IEEE. Transfer learning is gaining considerable attention due to its ability to leverage previously acquired knowledge to assist in completing a prediction task in a related domain. Fuzzy transfer learning, which is based on fuzzy system (especially fuzzy rule-based models), has been developed because of its capability to deal with the uncertainty in transfer learning. However, two issues with fuzzy transfer learning have not yet been resolved: choosing an appropriate source domain and efficiently selecting labeled data for the target domain. This paper proposes an innovative method based on fuzzy rules that combines an infinite Gaussian mixture model (IGMM) with active learning to enhance the performance and generalizability of the constructed model. An IGMM is used to identify the data structures in the source and target domains providing a promising solution to the domain selection dilemma. Further, we exploit the interactive query strategy in active learning to correct imbalances in the knowledge to improve the generalizability of fuzzy learning models. Through experiments on synthetic datasets, we demonstrate the rationality of employing an IGMM and the effectiveness of applying an active learning technique. Additional experiments on real-world datasets further support the capabilities of the proposed method in practical situations.
Zuo, H, Lu, J, Zhang, G & Pedrycz, W 2019, 'Fuzzy Rule-Based Domain Adaptation in Homogeneous and Heterogeneous Spaces', IEEE Transactions on Fuzzy Systems, vol. 27, no. 2, pp. 348-361.View/Download from: UTS OPUS or Publisher's site
© 2018 IEEE. Domain adaptation aims to leverage knowledge acquired from a related domain (called a source domain) to improve the efficiency of completing a prediction task (classification or regression) in the current domain (called the target domain), which has a different probability distribution from the source domain. Although domain adaptation has been widely studied, most existing research has focused on homogeneous domain adaptation, where both domains have identical feature spaces. Recently, a new challenge proposed in this area is heterogeneous domain adaptation where both the probability distributions and the feature spaces are different. Moreover, in both homogeneous and heterogeneous domain adaptation, the greatest efforts and major achievements have been made with classification tasks, while successful solutions for tackling regression problems are limited. This paper proposes two innovative fuzzy rule-based methods to deal with regression problems. The first method, called fuzzy homogeneous domain adaptation, handles homogeneous spaces while the second method, called fuzzy heterogeneous domain adaptation, handles heterogeneous spaces. Fuzzy rules are first generated from the source domain through a learning process; these rules, also known as knowledge, are then transferred to the target domain by establishing a latent feature space to minimize the gap between the feature spaces of the two domains. Through experiments on synthetic datasets, we demonstrate the effectiveness of both methods and discuss the impact of some of the significant parameters that affect performance. Experiments on real-world datasets also show that the proposed methods improve the performance of the target model over an existing source model or a model built using a small amount of target data.
Zuo, H, Zhang, G, Pedrycz, W, Behbood, V & Lu, J 2018, 'Granular Fuzzy Regression Domain Adaptation in Takagi-Sugeno Fuzzy Models', IEEE Transactions on Fuzzy Systems, vol. 26, no. 2, pp. 847-858.View/Download from: UTS OPUS or Publisher's site
© 1993-2012 IEEE. 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.
Zuo, H, Zhang, G, Pedrycz, W, Behbood, V & Lu, J 2017, 'Fuzzy Regression Transfer Learning in Takagi-Sugeno Fuzzy Models', IEEE Transactions on Fuzzy Systems, vol. 25, no. 6, pp. 1795-1807.View/Download from: UTS OPUS or Publisher's site
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.
Lu, J, Behbood, V, Hao, P, Zuo, H, Xue, S & Zhang, G 2015, 'Transfer Learning using Computational Intelligence: A Survey', Knowledge-Based Systems, vol. 80, pp. 14-23.View/Download from: UTS OPUS or Publisher's site
Abstract Transfer learning aims to provide a framework to utilize previously-acquired
knowledge to solve new but similar problems much more quickly and effectively. In contrast
to classical machine learning methods, transfer learning methods exploit the knowledge
accumulated from data in auxiliary domains to facilitate predictive modeling consisting of
different data patterns in the current domain. To improve the performance of existing transfer
learning methods and handle the knowledge transfer process in real-world systems, ...
Zuo, H, Zhang, G & Lu, J 2018, 'Fuzzy domain adaptation using unlabeled target data', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), International Conference on Neural Information Processing, Springer Link, Siem Reap, Cambodia, pp. 242-250.View/Download from: UTS OPUS or Publisher's site
© Springer Nature Switzerland AG 2018. Transfer learning has been emerging recently and gaining more attention because of its ability to deal with 'small labeled data' issue in new markets and for new products. It addresses the problem of leveraging knowledge acquired from previous domain (a source domain with a large amount of labeled data) to improve the accuracy of tasks in the current domain (a target domain with little labeled data). Fuzzy rule-based transfer learning methods are developed due to the ability to dealing with the uncertainty in domain adaptation scenarios. Although some effort is made to develop the fuzzy methods, they only apply the knowledge of the labeled data in the target domain to assist the model's construction. This work develops a new method that explores and utilizes the information contained in the unlabeled target data to improve the performance of the new constructed model. The experiments on both synthetic datasets and real-world datasets illustrate the effectiveness of our method, and also give the application scope of applying it.
Zuo, H, Zhang, G & Lu, J 2018, 'Semi-supervised transfer learning in Takagi-Sugeno fuzzy models', Data Science and Knowledge Engineering for Sensing Decision Support, Conference on Data Science and Knowledge Engineering for Sensing Decision Support (FLINS 2018), WORLD SCIENTIFIC.View/Download from: UTS OPUS or Publisher's site
Zuo, H, Zhang, G, Lu, J & Pedrycz, W 2017, 'Fuzzy Rule-based Transfer Learning for Label Space Adaptation', 2017 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS (FUZZ-IEEE), IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), IEEE, Naples, ITALY.View/Download from: UTS OPUS
Zuo, H, Zhang, G, Behbood, V, Lu, J, Pedrycz, W & Zhang, T 2016, 'FUZZY TRANSFER LEARNING IN DATA-SHORTAGE AND RAPIDLY CHANGING ENVIRONMENTS', UNCERTAINTY MODELLING IN KNOWLEDGE ENGINEERING AND DECISION MAKING, 12th International Conference on Fuzzy Logic and Intelligent Technologies in Nuclear Science (FLINS), WORLD SCIENTIFIC PUBL CO PTE LTD, Roubaix, FRANCE, pp. 175-180.
Zuo, H, Zhang, G, Behbood, V & Lu, J 2015, 'Feature Spaces-based Transfer Learning', PROCEEDINGS OF THE 2015 CONFERENCE OF THE INTERNATIONAL FUZZY SYSTEMS ASSOCIATION AND THE EUROPEAN SOCIETY FOR FUZZY LOGIC AND TECHNOLOGY, World Congress of the International-Fuzzy-Systems-Association (IFSA) / Conference of the European-Society-for-Fuzzy-Logic-and-Technology (EUSFLAT), Atlantis Press, Gijon, Spain, pp. 1000-1005.View/Download from: UTS OPUS or Publisher's site
Transfer learning provides an approach to solve target tasks more quickly and effectively by using previously-acquired knowledge learned from source tasks. Most of transfer learning approaches extract knowledge of source domain in the given feature space. The issue is that single perspective can t mine the relationship of source domain and target domain fully. To deal with this issue, this paper develops a method using Stacked Denoising Autoencoder (SDA) to extract new feature spaces for source domain and target domain, and define two fuzzy sets to analyse the variation of prediction ac-curacy of target task in new feature spaces
Zuo, H, Zhang, G, Behbood, V, Lu, J & Meng, X 2015, 'Transfer Learning in Hierarchical Feature Spaces', Procedings of the 10th International Conference on Intelligent Systems and Knowledge Engineering, International Conference on Intelligent Systems and Knowledge Engineering, IEEE, Taipei, Taiwan, pp. 183-188.View/Download from: UTS OPUS or Publisher's site
Transfer learning provides an approach to solve target tasks more quickly and effectively by using previously acquired knowledge learned from source tasks. As one category of transfer learning approaches, feature-based transfer learning approaches aim to find a latent feature space shared between source and target domains. The issue is that the sole feature space can't exploit the relationship of source domain and target domain fully. To deal with this issue, this paper proposes a transfer learning method that uses deep learning to extract hierarchical feature spaces, so knowledge of source domain can be exploited and transferred in multiple feature spaces with different levels of abstraction. In the experiment, the effectiveness of transfer learning in multiple feature spaces is compared and this can help us find the optimal feature space for transfer learning