Transfer learning using computational intelligence: a survey

Jie Lu*, Vahid Behbood, Peng Hao, Hua Zuo, Shan Xue, Guangquan Zhang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

598 Citations (Scopus)


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, computational intelligence has recently been applied in transfer learning. This paper systematically examines computational intelligence-based transfer learning techniques and clusters related technique developments into four main categories: (a) neural network-based transfer learning; (b) Bayes-based transfer learning; (c) fuzzy transfer learning, and (d) applications of computational intelligence-based transfer learning. By providing state-of-the-art knowledge, this survey will directly support researchers and practice-based professionals to understand the developments in computational intelligence-based transfer learning research and applications.

Original languageEnglish
Pages (from-to)14-23
Number of pages10
JournalKnowledge-Based Systems
Publication statusPublished - May 2015
Externally publishedYes


  • Bayes
  • Computational intelligence
  • Fuzzy sets and systems
  • Genetic algorithm
  • Neural network
  • Transfer learning


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