@Article{JFBI-8-1,
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 = {https://global-sci.com/article/86535/ensemble-inductive-transfer-learning}
}