NDLI: Marine literature categorization based on minimizing the labelled data

Content Provider	IEEE Xplore Digital Library
Author	Wei Zhang Qiuhong Wang Ye Deng Ranran Du
Copyright Year	2010
Description	Author affiliation: Department of Computer Science and Technology, Ocean University of China, Qing Dao, Shandong Province, China (Wei Zhang; Qiuhong Wang; Ye Deng; Ranran Du)
Abstract	In marine literature categorization, supervised machine learning method will take a lot of time for labelling the samples by hand. So we utilize Co-training method to decrease the quantities of labelled samples needed for training the classifier. In this paper, we only select features from the text details and add attribute labels to them. It can greatly boost the efficiency of text processing. For building up two views, we split features into two parts, each of which can form an independent view. One view is made up of the feature set of abstract, and the other is made up of the feature sets of title, keywords, creator and department. In experiments, the F1 value and error rate of the categorization system could reach about 0.863 and 14.26%.They are close to the performance of supervised classifier (0.902 and 9.13%), which is trained by more than 1500 labelled samples, however, the labelled samples used by Co-training categorization method to train the original classifier are only one positive sample and one negative sample. In addition we consider joining the idea of the active-learning in Co-training method.
Starting Page	1
Ending Page	6
File Size	1036573
Page Count	6
File Format	PDF
ISBN	9781424468966
e-ISBN	9781424468997
DOI	10.1109/NLPKE.2010.5587847
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-08-21
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Co-training active-learning Manuals two views Variable speed drives Marine literature categorization
Content Type	Text
Resource Type	Article

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