Research Repository

Learning from multiple related data streams with asynchronous flowing speeds

Qiao, Zhi and Zhang, Peng and He, Jing and Yan, Jinghua and Guo, Li (2010) Learning from multiple related data streams with asynchronous flowing speeds. In: Ninth International Conference on Machine Learning and Applications (ICMLA), 2010 : 12 - 14 Dec. 2010, Washington, DC, USA, Proceedings. Draghici, Sorin and Khoshgoftaar, Taghi M and Palade, Vasile and Pedrycz, Witold and Wani, M. Arif and Zhu, Xingquan, eds. IEEE, Piscataway, N.J., pp. 272-277.

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Abstract

Related data streams refer to data streams that can be joined together by matching their join attributes. Existing research on learning from related data streams is based on an assumption that all streams arrive at a central processing unit in a synchronous way, such that in an arbitrary sliding window, all tuples of the streams can be perfectly joined together. This assumption, however, does not hold when related data streams are generated or transferred at different speeds, and thus may arrive in the central processing unit in an asynchronous manner. In this paper, we argue that for asynchronous data streams, there exist a small portion of perfectly joined examples (i.e., complete examples) and a large portion of partially joined examples (i.e., incomplete examples). Accordingly, we present a new Learning from Complete and Fixed Examples (LCFE) framework that can fix incomplete examples to boost the learning. Experiments on both synthetic and real-world data streams demonstrate that LCFE is able to achieve a higher prediction accuracy for learning from related data streams than other simple solutions can offer.

Item Type: Book Section
ISBN: 9781424492114 (print), 9780769543000
Uncontrolled Keywords: ResPubID21684, data stream mining, learn from complete and incomplete examples, LCIE, UCI machine learning repository
Subjects: FOR Classification > 0801 Artificial Intelligence and Image Processing
Faculty/School/Research Centre/Department > School of Engineering and Science
Depositing User: VUIR
Date Deposited: 16 May 2013 06:26
Last Modified: 15 Dec 2014 05:29
URI: http://vuir.vu.edu.au/id/eprint/9971
DOI: 10.1109/ICMLA.2010.47
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Citations in Scopus: 0 - View on Scopus

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