Batch mode active learning for networked data with optimal subset selection

Haihui Xu, Pengpeng Zhao, Victor S. Sheng, Guanfeng Liu, Lei Zhao, Jian Wu, Zhiming Cui

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Active learning has increasingly become an important paradigm for classification of networked data, where instances are connected with a set of links to form a network. In this paper, we propose a novel batch mode active learning method for networked data (BMALNeT). Our novel active learning method selects the best subset of instances from the unlabeled set based on the correlation matrix that we construct from the dedicated informativeness evaluation of each unlabeled instance. To evaluate the informativeness of each unlabeled instance accurately, we simultaneously exploit content information and the network structure to capture the uncertainty and representativeness of each instance and the disparity between any two instances. Compared with state-of-the-art methods, our experimental results on three realworld datasets demonstrate the effectiveness of our proposed method.

Original languageEnglish
Title of host publicationWeb-Age Information Management - 16th International Conference, WAIM 2015, Proceedings
EditorsYizhou Sun, Jian Li
PublisherSpringer-Verlag
Pages96-108
Number of pages13
ISBN (Electronic)9783319210414
DOIs
StatePublished - 2015
Event16th International Conference on Web-Age Information Management, WAIM 2015 - Qingdao, China
Duration: Jun 8 2015Jun 10 2015

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9098
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference16th International Conference on Web-Age Information Management, WAIM 2015
CountryChina
CityQingdao
Period06/8/1506/10/15

Keywords

  • Active learning
  • Batch mode
  • Correlation matrix
  • Optimal subset

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