NDLI: A robust one-class transfer learning method with uncertain data

Content Provider	Springer Nature Link
Author	Xiao, Yanshan Liu, Bo Yu, Philip S. Hao, Zhifeng
Copyright Year	2014
Abstract	One-class classification aims at constructing a distinctive classifier based on one class of examples. Most of the existing one-class classification methods are proposed based on the assumptions that: (1) there are a large number of training examples available for learning the classifier; (2) the training examples can be explicitly collected and hence do not contain any uncertain information. However, in real-world applications, these assumptions are not always satisfied. In this paper, we propose a novel approach called uncertain one-class transfer learning with support vector machine (UOCT-SVM), which is capable of constructing an accurate classifier on the target task by transferring knowledge from multiple source tasks whose data may contain uncertain information. In UOCT-SVM, the optimization function is formulated to deal with uncertain data and transfer learning based on one-class SVM. Then, an iterative framework is proposed to solve the optimization function. Extensive experiments have showed that UOCT-SVM can mitigate the effect of uncertain data on the decision boundary and transfer knowledge from source tasks to help build an accurate classifier on the target task, compared with state-of-the-art one-class classification methods.
Starting Page	407
Ending Page	438
Page Count	32
File Format	PDF
ISSN	02191377
Journal	Knowledge and Information Systems
Volume Number	44
Issue Number	2
e-ISSN	02193116
Language	English
Publisher	Springer London
Publisher Date	2014-07-29
Publisher Place	London
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Transfer learning Uncertain data One-class classification Information Systems and Communication Service IT in Business
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Information Systems Human-Computer Interaction Hardware and Architecture Software

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