Social stream classification with emerging new labels
As an important research topic with well-recognized practical values, classification of social streams has been identified with increasing popularity with social data, such as the tweet stream generated by Twitter users in chronological order. A salient, and perhaps also the most interesting, featur...
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sg-smu-ink.sis_research-50822019-06-28T00:46:45Z Social stream classification with emerging new labels MU, Xin ZHU, Feida LIU, Yue LIM, Ee-peng ZHOU, Zhi-Hua As an important research topic with well-recognized practical values, classification of social streams has been identified with increasing popularity with social data, such as the tweet stream generated by Twitter users in chronological order. A salient, and perhaps also the most interesting, feature of such user-generated content is its never-failing novelty, which, unfortunately, would challenge most traditional pre-trained classification models as they are built based on fixed label set and would therefore fail to identify new labels as they emerge. In this paper, we study the problem of classification of social streams with emerging new labels, and propose a novel ensemble framework, integrating an instance-based learner and a label-based learner by completely-random trees. The proposed framework can not only classify known labels in the multi-label scenario, but also detect emerging new labels and update itself in the data stream. Extensive experiments on real-world stream data set from Weibo, a Chinese micro-blogging platform, demonstrate the superiority of our approach over the state-of-the-art methods. 2018-06-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/4079 info:doi/10.1007/978-3-319-93034-3_2 https://ink.library.smu.edu.sg/context/sis_research/article/5082/viewcontent/Mu2018_Chapter_SocialStreamClassificationWith.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Stream classification Emerging new labels Model update Databases and Information Systems Numerical Analysis and Scientific Computing |
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As an important research topic with well-recognized practical values, classification of social streams has been identified with increasing popularity with social data, such as the tweet stream generated by Twitter users in chronological order. A salient, and perhaps also the most interesting, feature of such user-generated content is its never-failing novelty, which, unfortunately, would challenge most traditional pre-trained classification models as they are built based on fixed label set and would therefore fail to identify new labels as they emerge. In this paper, we study the problem of classification of social streams with emerging new labels, and propose a novel ensemble framework, integrating an instance-based learner and a label-based learner by completely-random trees. The proposed framework can not only classify known labels in the multi-label scenario, but also detect emerging new labels and update itself in the data stream. Extensive experiments on real-world stream data set from Weibo, a Chinese micro-blogging platform, demonstrate the superiority of our approach over the state-of-the-art methods. |
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MU, Xin ZHU, Feida LIU, Yue LIM, Ee-peng ZHOU, Zhi-Hua |
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MU, Xin ZHU, Feida LIU, Yue LIM, Ee-peng ZHOU, Zhi-Hua |
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MU, Xin |
title |
Social stream classification with emerging new labels |
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Social stream classification with emerging new labels |
title_full |
Social stream classification with emerging new labels |
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Social stream classification with emerging new labels |
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Social stream classification with emerging new labels |
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social stream classification with emerging new labels |
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Institutional Knowledge at Singapore Management University |
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2018 |
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https://ink.library.smu.edu.sg/sis_research/4079 https://ink.library.smu.edu.sg/context/sis_research/article/5082/viewcontent/Mu2018_Chapter_SocialStreamClassificationWith.pdf |
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