An output-based knowledge transfer approach and its application in bladder cancer prediction

Guanjin Wang, Guangquan Zhang, Kup Sze Choi, Kin Man Lam, Jie Lu

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

1 Citation (Scopus)


Many medical applications face a situation that the on-hand data cannot fully fit an existing predictive model or on-line tool, since these models or tools only use the most common predictors and the other valuable features collected in the current scenario are not considered altogether. On the other hand, the training data in the current scenario is not sufficient to learn a predictive model effectively yet. In order to overcome these problems and construct an efficient classifier, for these real situations in medical fields, in this work we present an approach based on the least squares support vector machine (LS-SVM), which utilizes a transfer learning framework to make maximum use of the data and guarantee its enhanced generalization capability. The proposed approach is capable of effectively learning a target domain with limited samples by relying on the probabilistic outputs from the other previously learned model using a heterogeneous method in the source domain. Moreover, it autonomously and quickly decides how much output knowledge to transfer from source domain to the target one using a fast leave-one-out cross validation strategy. This approach is applied on a real-world clinical dataset to predict 5-year mortality of bladder cancer patients after radical cystectomy, and the experimental results indicate that the proposed method can achieve better performances compared to traditional machine learning methods, consistently showing the potential of the proposed method under the circumstances with insufficient data.
Original languageEnglish
Title of host publication2017 International Joint Conference on Neural Networks, IJCNN 2017 - Proceedings
Number of pages8
ISBN (Electronic)9781509061815
Publication statusPublished - 30 Jun 2017
Event2017 International Joint Conference on Neural Networks, IJCNN 2017 - Anchorage, United States
Duration: 14 May 201719 May 2017


Conference2017 International Joint Conference on Neural Networks, IJCNN 2017
Country/TerritoryUnited States


  • Cancer prediction
  • Machine learning
  • Support vector machine
  • Transfer learning

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

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