NDLI: A Tri-training Based Transfer Learning Algorithm

Content Provider	IEEE Xplore Digital Library
Author	Xiaobo Liu Zhang, H. Zhihua Cai Guangjun Wang
Copyright Year	2012
Description	Author affiliation: Exp. Teaching Center of Inf. Technol., China Univ. of Geosci., Wuhan, China (Guangjun Wang) \|\| Fac. of Comput. Sci., Univ. of New Brunswick, Fredericton, NB, Canada (Zhang, H.) \|\| Sch. of Comput. Sci., China Univ. of Geosci., Wuhan, China (Xiaobo Liu; Zhihua Cai)
Abstract	The lack of labeled training data is a common issue in many machine learning applications. Semi-supervised learning addresses this issue by self-labeling unlabelled examples. Transfer learning tackles it from a different way: borrow labeled examples from a different but related domain (source domain) by assigning weights to those examples based on their suitability on the new domain (target domain). However, it is quite challenging to figure out the suitability. In this paper, we propose a different way for utilizing the labeled examples from source domain. That is, we use them only for labelling the unlabelled examples in the target domain. In this self-labelling, we use the idea of Tri-training. We call our new algorithm: TriTransfer. In TriTransfer, three initial classifiers are generated from the source data and the originally labeled data in the target domain, and an unlabeled example is labeled and added to the labeled data for a classifier if other two classifiers agree on its label. After an expanded labeled data set is obtained, we re-train the classifier. We repeat this process until no more change can be made. At the end, the final classifier, which is a weighted combination of the three classifiers, is output. We conduct an extensive empirical study on 34 UCI datasets, which shows that TriTransfer performs better than the state-of-art algorithms Transfer Boost, Tritraining, and NaiveBayes.
Starting Page	698
Ending Page	703
File Size	317238
Page Count	6
File Format	PDF
ISBN	9781479902279
ISSN	10823409
DOI	10.1109/ICTAI.2012.99
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-11-07
Publisher Place	Greece
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Semisupervised learning Training Accuracy Training data Labeling Educational institutions Machine learning algorithms machine learning transfer learning ensemble learning
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Libarray of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Cost sensitive active learning based on self-training

Tri-Training Based Learning from Positive and Unlabeled Data

Transfer Ensemble Model for Customer Churn Prediction with Imbalanced Class Distribution

An improved algorithm for relation extraction based on tri-training

A new classification algorithm based on ensemble PSO_SVM and clustering analysis

Tri-training: exploiting unlabeled data using three classifiers

CoTrade: Confident Co-Training With Data Editing

On Co-Training Style Algorithms

Semi-supervised cluster ensemble based on binary similarity matrix

A Tri-training Based Transfer Learning Algorithm

Similar Documents

Cost sensitive active learning based on self-training

Tri-Training Based Learning from Positive and Unlabeled Data

Transfer Ensemble Model for Customer Churn Prediction with Imbalanced Class Distribution

An improved algorithm for relation extraction based on tri-training

A new classification algorithm based on ensemble PSO_SVM and clustering analysis

Tri-training: exploiting unlabeled data using three classifiers

CoTrade: Confident Co-Training With Data Editing

On Co-Training Style Algorithms

Semi-supervised cluster ensemble based on binary similarity matrix

A Tri-training Based Transfer Learning Algorithm