Abstract
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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Notes
- 1.
The fewer partitions means that instances with new labels are more likely to be of the shorter height in each tree.
- 2.
This is a trade-off parameter, the larger means method needs more memory. In practise, we use the value which is greater than \(\psi \) to guide the setup of this parameter.
- 3.
- 4.
Here “\(\downarrow \)” means the smaller the value, the better the performance; and “\(\uparrow \)” means the larger the value, the better the performance.
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Acknowledgement
This research was supported by the National Research Foundation, Prime Minister’s Office, Singapore under its International Research Centres in Singapore Funding Initiative; the NSFC (61333014) and Pinnacle lab for analytics at Singapore Management University.
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Mu, X., Zhu, F., Liu, Y., Lim, EP., Zhou, ZH. (2018). Social Stream Classification with Emerging New Labels. In: Phung, D., Tseng, V., Webb, G., Ho, B., Ganji, M., Rashidi, L. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2018. Lecture Notes in Computer Science(), vol 10937. Springer, Cham. https://doi.org/10.1007/978-3-319-93034-3_2
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