Novel class detection within classification for data streams

Authors:
Yuqing Miao;Liangpei Qiu;Hong Chen;Jingxin Zhang;Yimin Wen
Affiliations:
School of Computer Science and Engineering, Guilin University of Electronic Technology, Guilin, China;School of Computer Science and Engineering, Guilin University of Electronic Technology, Guilin, China;School of Computer Science and Engineering, Guilin University of Electronic Technology, Guilin, China;School of Computer Science and Engineering, Guilin University of Electronic Technology, Guilin, China;School of Computer Science and Engineering, Guilin University of Electronic Technology, Guilin, China
Venue:
ISNN'13 Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part II
Year:
2013

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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.