NDLI: Improving sparsely labeled text classification with data editing

Content Provider	IEEE Xplore Digital Library
Author	Xue Zhang Dong-yan Zhao Wang-xin Xiao
Copyright Year	2010
Description	Author affiliation: Research Institute of Highway, Ministry of Transport, Beijing 100088, China (Wang-xin Xiao) \|\| Institute of Computer Science & Engineering, Peking University, Beijing 100871, China (Xue Zhang; Dong-yan Zhao)
Abstract	In this paper, an active semi-supervised framework combining with data editing is proposed to improve sparsely labeled text classification. It integrates semi-supervised learning with active learning, and fully utilizes the advantage of active learning by fusing it with a data editing technique. The algorithm works in an iterative fashion in which the steps of self-labeling, active labeling and editing are iterated alternatively. Active learning and data editing techniques are designed to cope with the training data bias and sparsity. According to our knowledge, the fusion of active learning with data editing technique to eliminate self-labeled noise is novel. Extensive experimental study on several real-world data sets shows the encouraging results of the proposed text classification framework for sparsely labeled text classification compared with several state-of-the-art methods.
Starting Page	3774
Ending Page	3777
File Size	397800
Page Count	4
File Format	PDF
ISBN	9781424476169
e-ISBN	9781424476183
DOI	10.1109/ICISE.2010.5690328
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-12-04
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Algorithm design and analysis Support vector machines Text categorization Active learning Semi-supervised learning Training data Data editing Sparsely labeled text classification Classification algorithms Labeling Nearest neighbor searches
Content Type	Text
Resource Type	Article

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