Abstract
Traditional data stream classification techniques are not capable of recognizing new classes emerged in data stream. Recently, an ensemble classification framework focused on the new challenge. But the novel class detection technique is limited to the numeric data in the framework. And, both the lower process speed and the larger model size of base classifier trouble the framework. In this paper, a novel class instance detection technique is proposed to deal with mixed attribute data and the VFDTc is adopted as base classifier to speed up the process and reduce the model size. Experimental results showed that the algorithm outperformed the previous one in both classification accuracy and processing speed.
The work described in this paper is supported by Foundation of Guangxi Key Laboratory of Trustworthy Software, China (kx201116) and Educational Commission of Guangxi Province, China(201204LX122).
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Miao, Y., Qiu, L., Chen, H., Zhang, J., Wen, Y. (2013). Novel Class Detection within Classification for Data Streams. In: Guo, C., Hou, ZG., Zeng, Z. (eds) Advances in Neural Networks – ISNN 2013. ISNN 2013. Lecture Notes in Computer Science, vol 7952. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39068-5_50
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DOI: https://doi.org/10.1007/978-3-642-39068-5_50
Publisher Name: Springer, Berlin, Heidelberg
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