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FUSION: An Online Method for Multistream Classification

Published: 06 November 2017 Publication History

Abstract

Traditional data stream classification assumes that data is generated from a single non-stationary process. On the contrary, multistream classification problem involves two independent non-stationary data generating processes. One of them is the source stream that continuously generates labeled data. The other one is the target stream that generates unlabeled test data from the same domain. The distribution represented by the source stream data is biased compared to that of the target stream. Moreover, these streams may have asynchronous concept drifts between them. The multistream classification problem is to predict the class labels of target stream instances by utilizing labeled data from the source stream. This kind of scenario is often observed in real-world applications due to scarcity of labeled data. The only existing approach for multistream classification uses separate drift detection on the streams for addressing the asynchronous concept drift problem. If a concept drift is detected in any of the streams, it uses an expensive batch technique for data shift adaptation. These add significant execution overhead, and limit its usability. In this paper, we propose an efficient solution for multistream classification by fusing drift detection into online data shift adaptation. We study the theoretical convergence rate and computational complexity of the proposed approach. Moreover, empirical results on benchmark data sets indicate significantly improved performance over the baseline methods.

References

[1]
Albert Bifet. 2009. Adaptive Learning and Mining for Data Streams and Frequent Patterns. SIGKDD Explor. Newsl. Vol. 11, 1 (Nov. 2009), 55--56. binfopersonTaiji Suzuki, Shinichi Nakajima, Hisashi Kashima, Paul von Bünau, and Motoaki Kawanabe. 2008. Direct importance estimation for covariate shift adaptation. Annals of the Institute of Statistical Mathematics, Vol. 60, 4 (2008), 699--746.
[2]
K. Tumer and J. Ghosh. 1996. Error correlation and error reduction in ensemble classifiers. Connection Science, Vol. 8, 3--4 (1996), 385--403.
[3]
Bianca Zadrozny Zadrozny. 2004. Learning and Evaluating Classifiers under Sample Selection Bias International Conference on Machine Learning (ICML). 903--910.

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cover image ACM Conferences
CIKM '17: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management
November 2017
2604 pages
ISBN:9781450349185
DOI:10.1145/3132847
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 06 November 2017

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Author Tags

  1. asynchronous concept drift
  2. data shift adaptation
  3. direct density ratio estimation
  4. multistream classification

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CIKM '17 Paper Acceptance Rate 171 of 855 submissions, 20%;
Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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  • (2024)Business Models Drive Investor Reactions in Construction SectorIndonesian Journal of Law and Economics Review10.21070/ijler.v19i2.109819:2Online publication date: 31-May-2024
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