Active learning via query synthesis and nearest neighbour search

Wang, Liantao, Hu, Xuelei, Yuan, Bo and Lu, Jianfeng (2013) Active learning via query synthesis and nearest neighbour search. Neurocomputing, 147 1: 426-434. doi:10.1016/j.neucom.2014.06.042

Attached Files (Some files may be inaccessible until you login with your UQ eSpace credentials)
Name Description MIMEType Size Downloads
UQ344582_OA.pdf Full text (open access) application/pdf 717.28KB 0

Author Wang, Liantao
Hu, Xuelei
Yuan, Bo
Lu, Jianfeng
Title Active learning via query synthesis and nearest neighbour search
Journal name Neurocomputing   Check publisher's open access policy
ISSN 0925-2312
Publication date 2013-08-22
Sub-type Article (original research)
DOI 10.1016/j.neucom.2014.06.042
Open Access Status Not yet assessed
Volume 147
Issue 1
Start page 426
End page 434
Total pages 9
Place of publication Amsterdam, Netherlands
Publisher Elsevier
Language eng
Subject 1706 Computer Science Applications
2805 Cognitive Neuroscience
1702 Artificial Intelligence
Abstract Active learning has received great interests from researchers due to its ability to reduce the amount of supervision required for effective learning. As the core component of active learning algorithms, query synthesis and pool-based sampling are two main scenarios of querying considered in the literature. Query synthesis features low querying time, but only has limited applications as the synthesized query might be unrecognizable to human oracle. As a result, most efforts have focused on pool-based sampling in recent years, although it is much more time-consuming. In this paper, we propose new strategies for a novel querying framework that combines query synthesis and pool-based sampling. It overcomes the limitation of query synthesis, and has the advantage of fast querying. The basic idea is to synthesize an instance close to the decision boundary using labelled data, and then select the real instance closest to the synthesized one as a query. For this purpose, we propose a synthesis strategy, which can synthesize instances close to the decision boundary and spreading along the decision boundary. Since the synthesis only depends on the relatively small labelled set, instead of evaluating the entire unlabelled set as many other active learning algorithms do, our method has the advantage of efficiency. In order to handle more complicated data and make our framework compatible with powerful kernel-based learners, we also extend our method to kernel version. Experiments on several real-world data sets show that our method has significant advantage on time complexity and similar performance compared to pool-based uncertainty sampling methods.
Keyword Active learning
Kernel function
Pool-based sampling
Query synthesis
Q-Index Code C1
Q-Index Status Provisional Code
Grant ID 61233011
Institutional Status Non-UQ

Document type: Journal Article
Sub-type: Article (original research)
Collection: School of Information Technology and Electrical Engineering Publications
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 5 times in Thomson Reuters Web of Science Article | Citations
Scopus Citation Count Cited 5 times in Scopus Article | Citations
Google Scholar Search Google Scholar
Created: Thu, 06 Nov 2014, 19:54:37 EST by Xuelei Hu on behalf of School of Information Technol and Elec Engineering