Social Stream Classification with Emerging New Labels
Social Stream Classification with Emerging New Labels
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DOI：
10.1007/978-3-319-93034-3_2
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发表时间：
2018-06
期刊：
影响因子：
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通讯作者：
Xin Mu;Feida Zhu;Yue Liu;Ee-Peng Lim;Zhi-Hua Zhou
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文献类型：
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作者：
Xin Mu;Feida Zhu;Yue Liu;Ee-Peng Lim;Zhi-Hua Zhou
作为一个重要的研究课题与公认的实用价值，社会流的分类已被确定与日益流行的社会数据，如Twitter用户产生的推文流按时间顺序。这种用户生成内容的一个突出的，也许也是最有趣的特征是它永不过时的新奇，不幸的是，这将挑战大多数传统的预训练分类模型，因为它们是基于固定标签集构建的，因此无法识别新标签。在本文中，我们研究了社会流与新兴的新标签的分类问题，并提出了一种新的集成框架，集成基于实例的学习器和基于标签的学习器的完全随机树。该框架不仅可以在多标签场景中对已知标签进行分类，还可以在数据流中检测新标签并进行自我更新。在真实世界的流数据集上进行的大量实验表明，该方法优于现有方法.
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 fromWeibo, a Chinese micro-blogging platform, demonstrate the superiority of our approach over the state-of-the-art methods.