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Proceedings Paper

Combining topological analysis matrices-based active learning on networked data classification
Author(s): Xiaoqi He; Yangguang Liu; Xiaogang Jin
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Paper Abstract

Active learning is an important technique to improve the learned model using unlabeled data, when labeled data is difficult to obtain, and unlabeled data is available in large quantity and easy to collect. Several instance querying strategies have been suggested recently. These works show that empirical risk minimization (ERM) can find the next instance to label effectively, but the computation time consumption is large. This paper introduces a new approach to select the best instance with less time consumption. In the case where the data is graphical in nature, we can implement the graph topological analysis to rapidly select instances that are likely to be good candidates for labeling. This paper describes an approach of using degree of a node metric to identify the best instance next to label. We experiment on Zachary's Karate Club dataset and 20 newsgroups dataset with four binary classification tasks, the results show that the strategy of degree of a node has similar performance to ERM with less time consumption.

Paper Details

Date Published: 27 May 2011
PDF: 7 pages
Proc. SPIE 7997, Fourth International Seminar on Modern Cutting and Measurement Engineering, 79973C (27 May 2011); doi: 10.1117/12.888404
Show Author Affiliations
Xiaoqi He, Zhejiang Univ. (China)
Yangguang Liu, Zhejiang Univ. (China)
Xiaogang Jin, Zhejiang Univ. (China)


Published in SPIE Proceedings Vol. 7997:
Fourth International Seminar on Modern Cutting and Measurement Engineering
Jiezhi Xin; Lianqing Zhu; Zhongyu Wang, Editor(s)

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