Volume 8, Issue 1
Ensemble Inductive Transfer Learning

Xiaobo Liu, Guangjun Wang, Zhihua Cai & Harry Zhang

Journal of Fiber Bioengineering & Informatics, 8 (2015), pp. 105-115.

Published online: 2015-08

Preview Full PDF 641 2386
Export citation
  • Abstract

Inductive transfer learning is a major research area in transfer learning which aims at achieving a high performance in the target domain by inducing the useful knowledge from the source domain. By combining decisions from individual classifiers, ensemble learning can usually reduce variance and achieve higher accuracy than a single classifier. In this paper, we propose a novel Ensemble Inductive Transfer Learning (EITL) method. EITL builds a set of classifiers by recording the iterative process of knowledge transfer. In each iteration, it uses the classifier of the source domain, the base classifier of the target domain built on the initial labeled data, and the most recent classifier built on the updated labeled data, to classify unlabeled instances, and add some self-labeled instances to the labeled data, and then trains a new classifier. At the end, all the classifiers built in this process are used for classification. We conduct experiments on synthetic data sets and six UCI data sets, which show that EITL is an effective algorithm in terms of classification accuracy.

  • Keywords

Transfer Learning Ensemble Learning Machine Learning

  • AMS Subject Headings

  • Copyright

COPYRIGHT: © Global Science Press

  • Email address
  • BibTex
  • RIS
  • TXT
@Article{JFBI-8-105, author = {}, title = {Ensemble Inductive Transfer Learning}, journal = {Journal of Fiber Bioengineering and Informatics}, year = {2015}, volume = {8}, number = {1}, pages = {105--115}, abstract = {Inductive transfer learning is a major research area in transfer learning which aims at achieving a high performance in the target domain by inducing the useful knowledge from the source domain. By combining decisions from individual classifiers, ensemble learning can usually reduce variance and achieve higher accuracy than a single classifier. In this paper, we propose a novel Ensemble Inductive Transfer Learning (EITL) method. EITL builds a set of classifiers by recording the iterative process of knowledge transfer. In each iteration, it uses the classifier of the source domain, the base classifier of the target domain built on the initial labeled data, and the most recent classifier built on the updated labeled data, to classify unlabeled instances, and add some self-labeled instances to the labeled data, and then trains a new classifier. At the end, all the classifiers built in this process are used for classification. We conduct experiments on synthetic data sets and six UCI data sets, which show that EITL is an effective algorithm in terms of classification accuracy.}, issn = {2617-8699}, doi = {https://doi.org/10.3993/jfbi03201510}, url = {http://global-sci.org/intro/article_detail/jfbi/4690.html} }
TY - JOUR T1 - Ensemble Inductive Transfer Learning JO - Journal of Fiber Bioengineering and Informatics VL - 1 SP - 105 EP - 115 PY - 2015 DA - 2015/08 SN - 8 DO - http://doi.org/10.3993/jfbi03201510 UR - https://global-sci.org/intro/article_detail/jfbi/4690.html KW - Transfer Learning KW - Ensemble Learning KW - Machine Learning AB - Inductive transfer learning is a major research area in transfer learning which aims at achieving a high performance in the target domain by inducing the useful knowledge from the source domain. By combining decisions from individual classifiers, ensemble learning can usually reduce variance and achieve higher accuracy than a single classifier. In this paper, we propose a novel Ensemble Inductive Transfer Learning (EITL) method. EITL builds a set of classifiers by recording the iterative process of knowledge transfer. In each iteration, it uses the classifier of the source domain, the base classifier of the target domain built on the initial labeled data, and the most recent classifier built on the updated labeled data, to classify unlabeled instances, and add some self-labeled instances to the labeled data, and then trains a new classifier. At the end, all the classifiers built in this process are used for classification. We conduct experiments on synthetic data sets and six UCI data sets, which show that EITL is an effective algorithm in terms of classification accuracy.
Xiaobo Liu, Guangjun Wang, Zhihua Cai & Harry Zhang. (2019). Ensemble Inductive Transfer Learning. Journal of Fiber Bioengineering and Informatics. 8 (1). 105-115. doi:10.3993/jfbi03201510
Copy to clipboard
The citation has been copied to your clipboard