Fuzzy Domain Adaptation Using Unlabeled Target Data

  • Hua Zuo
  • Guangquan Zhang
  • Jie Lu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11303)


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.


Domain adaptation Transfer learning Machine learning Fuzzy rules Regression 



This work was supported by the Australian Research Council under DP 170101623.


  1. 1.
    Nasrabadi, N.M.: Pattern recognition and machine learning. J. Electron. Imaging 16(4), 049901 (2007)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Lu, J., Xuan, J., Zhang, G., Luo, X.: Structural property-aware multilayer network embedding for latent factor analysis. Pattern Recogn. 76, 228–241 (2018)CrossRefGoogle Scholar
  3. 3.
    Pan, S.J., Yang, Q.: A survey on transfer learning. IEEE Trans. Knowl. Data Eng. 22(10), 1345–1359 (2010)CrossRefGoogle Scholar
  4. 4.
    Lim, C.-H., Wan, Y., Ng, B.-P., See, C.-M.S.: A real-time indoor WiFi localization system utilizing smart antennas. IEEE Trans. Consum. Electron. 53(2) (2007)CrossRefGoogle Scholar
  5. 5.
    Xu, J., Ramos, S., Vázquez, D., López, A.M.: Domain adaptation of deformable part-based models. IEEE Trans. Pattern Anal. Mach. Intell. 36(12), 2367–2380 (2014)CrossRefGoogle Scholar
  6. 6.
    Long, M., Wang, J., Cao, Y., Sun, J., Philip, S.Y.: Deep learning of transferable representation for scalable domain adaptation. IEEE Trans. Knowl. Data Eng. 28(8), 2027–2040 (2016)CrossRefGoogle Scholar
  7. 7.
    Gönen, M., Margolin, A.A.: Kernelized Bayesian transfer learning. In: AAAI, pp. 1831–1839 (2014)Google Scholar
  8. 8.
    Klenk, M., Forbus, K.: Analogical model formulation for transfer learning in AP physics. Artif. Intell. 173(18), 1615–1638 (2009)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Bengio, Y.: Deep learning of representations for unsupervised and transfer learning. In Proceedings of ICML Workshop on Unsupervised and Transfer Learning, pp. 17–36 (2012)Google Scholar
  10. 10.
    Lu, J., Behbood, V., Hao, P., Zuo, H., Xue, S., Zhang, G.: Transfer learning using computational intelligence: a survey. Knowl.-Based Syst. 80, 14–23 (2015)CrossRefGoogle Scholar
  11. 11.
    Shao, L., Zhu, F., Li, X.: Transfer learning for visual categorization: a survey. IEEE Trans. Neural Netw. Learn. Syst. 26(5), 1019–1034 (2015)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Zuo, H., Zhang, G., Pedrycz, W., Behbood, V., Lu, J.: Fuzzy regression transfer learning in Takagi-Sugeno fuzzy models. IEEE Trans. Fuzzy Syst. 25(6), 1795–1807 (2017)CrossRefGoogle Scholar
  13. 13.
    Zuo, H., Zhang, G., Pedrycz, W., Behbood, V., Lu, J.: Granular fuzzy regression domain adaptation in Takagi-Sugeno Fuzzy models. IEEE Trans. Fuzzy Syst. 26(2), 847–858 (2017)CrossRefGoogle Scholar
  14. 14.
    Rasmussen, C.E.: The infinite Gaussian mixture model. In: Advances in Neural Information Processing Systems, pp. 554–560 (2000)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.DeSI Lab, Centre for Artificial Intelligence, FEITUniversity of Technology SydneyUltimoAustralia

Personalised recommendations