skip to main content
10.1145/2983323.2983842acmconferencesArticle/Chapter ViewAbstractPublication PagescikmConference Proceedingsconference-collections
research-article
Public Access

An Adaptive Framework for Multistream Classification

Published: 24 October 2016 Publication History

Abstract

A typical data stream classification involves predicting label of data instances generated from a non-stationary process. Studies in the past decade have focused on this problem setting to address various challenges such as concept drift and concept evolution. Most techniques assume availability of class labels associated with unlabeled data instances, soon after label prediction, for further training and drift detection. Moreover, training and test data distributions are assumed to be similar. These assumptions are not always true in practice. For instance, a semi-supervised setting that aims to utilize only a fraction of labels may induce bias during data selection. Consequently, the resulting data distribution of training and test instances may differ. In this paper, we present a novel stream classification problem setting involving two independent non-stationary data generating processes, relaxing the above assumptions. A source stream continuously generates labeled data instances whose distribution is biased compared to that of a target stream which generates unlabeled data instances from the same domain. The problem, we call Multistream Classification, is to predict the class labels of data instances in the target stream, while utilizing labels available on the source stream. Since concept drift can occur asynchronously on these two streams, we design an adaptive framework that uses a technique for supervised concept drift detection in the biased source stream, and unsupervised concept drift detection in the target stream. A weighted ensemble of classifiers is updated after each drift detection on either streams, while utilizing a bias correction mechanism that leverage source information to predict labels of target instances whenever necessary. We empirically evaluate the multistream classifier's performance on both real-world and synthetic datasets, while comparing with various baseline methods and its variants.

References

[1]
S. D. Bay, D. F. Kibler, M. J. Pazzani, and P. Smyth. The uci kdd archive of large data sets for data mining research and experimentation. In SIGKDD Explorations, 2000.
[2]
S. Ben-David, J. Blitzer, K. Crammer, A. Kulesza, F. Pereira, and J. W. Vaughan. A theory of learning from different domains. Machine learning, 79(1--2):151--175, 2010.
[3]
A. Bifet and R. Gavaldà. Learning from time-changing data with adaptive windowing. In SDM. SIAM, 2007.
[4]
A. Bifet, G. Holmes, B. Pfahringer, P. Kranen, H. Kremer, T. Jansen, and T. Seidl. Moa: Massive online analysis, a framework for stream classification and clustering. In Journal of Machine Learning Research, pages 44--50, 2010.
[5]
C.-C. Chang and C.-J. Lin. Libsvm: a library for support vector machines. ACM Transactions on Intelligent Systems and Technology (TIST), 2(3):27, 2011.
[6]
C. Cortes, M. Mohri, M. Riley, and A. Rostamizadeh. Sample selection bias correction theory. In Algorithmic learning theory, pages 38--53. Springer, 2008.
[7]
J. Dahl and L. Vandenberghe. Cvxopt: A python package for convex optimization. In Proc. eur. conf. op. res, 2006.
[8]
W. Dai, G.-R. Xue, Q. Yang, and Y. Yu. Co-clustering based classification for out-of-domain documents. In Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 210--219. ACM, 2007.
[9]
J. Gama, P. Medas, G. Castillo, and P. Rodrigues. Learning with drift detection. In Advances in artificial intelligence--SBIA 2004, pages 286--295. Springer, 2004.
[10]
J. Gama, I.vZliobait\.e, A. Bifet, M. Pechenizkiy, and A. Bouchachia. A survey on concept drift adaptation. ACM Computing Surveys (CSUR), 46(4):44, 2014.
[11]
A. Haque, L. Khan, and M. Baron. Sand: Semi-supervised adaptive novel class detection and classification over data stream. In Thirteenth AAAI Conference on Artificial Intelligence, pages 1652--1658, Feb 2016.
[12]
M. Harel, S. Mannor, R. El-yaniv, and K. Crammer. Concept drift detection through resampling. In ICML-14, pages 1009--1017. JMLR Workshop and Conference Proceedings, 2014.
[13]
J. Huang, A. Gretton, K. M. Borgwardt, B. Schölkopf, and A. J. Smola. Correcting sample selection bias by unlabeled data. In Advances in neural information processing systems, pages 601--608, 2006.
[14]
R. Klinkenberg. Learning drifting concepts: Example selection vs. example weighting. Intell. Data Anal., 8(3):281--300, Aug. 2004.
[15]
E. Kouloumpis, T. Wilson, and J. D. Moore. Twitter sentiment analysis: The good the bad and the omg! Icwsm, 11:538--541, 2011.
[16]
B. Li, Q. Yang, and X. Xue. Transfer learning for collaborative filtering via a rating-matrix generative model. In Proceedings of the 26th Annual International Conference on Machine Learning, pages 617--624. ACM, 2009.
[17]
M. M. Masud, J. Gao, L. Khan, J. Han, and B. M. Thuraisingham. A practical approach to classify evolving data streams: Training with limited amount of labeled data. In ICDM, pages 929--934, 2008.
[18]
Y.-Q. Miao, A. K. Farahat, and M. S. Kamel. Ensemble kernel mean matching. In Data Mining (ICDM), 2015 IEEE International Conference on, pages 330--338. IEEE, 2015.
[19]
S. J. Pan and Q. Yang. A survey on transfer learning. Knowledge and Data Engineering, IEEE Transactions on, 22(10):1345--1359, 2010.
[20]
S. J. Pan, V. W. Zheng, Q. Yang, and D. H. Hu. Transfer learning for wifi-based indoor localization. In Association for the advancement of artificial intelligence (AAAI) workshop, page 6, 2008.
[21]
J. C. Platt. Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. In Advances in Large Margin Classifiers, pages 61--74. MIT Press, 1999.
[22]
W. N. Street and Y. Kim. A streaming ensemble algorithm (sea) for large-scale classification. In Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining, pages 377--382. ACM, 2001.
[23]
M. Sugiyama, S. Nakajima, H. Kashima, P. V. Buenau, and M. Kawanabe. Direct importance estimation with model selection and its application to covariate shift adaptation. In Advances in neural information processing systems, pages 1433--1440, 2008.
[24]
V. N. Vapnik. Statistical Learning Theory. Wiley-Interscience, 1998.
[25]
H. Wang, W. Fan, P. S. Yu, and J. Han. Mining concept-drifting data streams using ensemble classifiers. In Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '03, pages 226--235, New York, NY, USA, 2003. ACM.
[26]
T.-F. Wu, C.-J. Lin, and R. C. Weng. Probability estimates for multi-class classification by pairwise coupling. J. Mach. Learn. Res., 5:975--1005, Dec. 2004.
[27]
Y.-l. Yu and C. Szepesvári. Analysis of kernel mean matching under covariate shift. In Proceedings of the 29th International Conference on Machine Learning (ICML-12), pages 607--614, 2012.

Cited By

View all
  • (2024)Dynamic Graph Regularization for Multi-Stream Concept Drift Self-AdaptationIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.340115636:11(6016-6028)Online publication date: Nov-2024
  • (2024)Fuzzy Shared Representation Learning for Multistream ClassificationIEEE Transactions on Fuzzy Systems10.1109/TFUZZ.2024.342302432:10(5625-5637)Online publication date: 1-Oct-2024
  • (2024)Heterogeneous Domain Adaptation for Multistream Classification on Cyber Threat DataIEEE Transactions on Dependable and Secure Computing10.1109/TDSC.2022.318168221:1(1-11)Online publication date: Jan-2024
  • Show More Cited By

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences
CIKM '16: Proceedings of the 25th ACM International on Conference on Information and Knowledge Management
October 2016
2566 pages
ISBN:9781450340731
DOI:10.1145/2983323
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]

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 24 October 2016

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. classification
  2. concept drift
  3. covariate shift
  4. data stream

Qualifiers

  • Research-article

Funding Sources

Conference

CIKM'16
Sponsor:
CIKM'16: ACM Conference on Information and Knowledge Management
October 24 - 28, 2016
Indiana, Indianapolis, USA

Acceptance Rates

CIKM '16 Paper Acceptance Rate 160 of 701 submissions, 23%;
Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

Upcoming Conference

CIKM '25

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)142
  • Downloads (Last 6 weeks)12
Reflects downloads up to 20 Feb 2025

Other Metrics

Citations

Cited By

View all
  • (2024)Dynamic Graph Regularization for Multi-Stream Concept Drift Self-AdaptationIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.340115636:11(6016-6028)Online publication date: Nov-2024
  • (2024)Fuzzy Shared Representation Learning for Multistream ClassificationIEEE Transactions on Fuzzy Systems10.1109/TFUZZ.2024.342302432:10(5625-5637)Online publication date: 1-Oct-2024
  • (2024)Heterogeneous Domain Adaptation for Multistream Classification on Cyber Threat DataIEEE Transactions on Dependable and Secure Computing10.1109/TDSC.2022.318168221:1(1-11)Online publication date: Jan-2024
  • (2024)Real-time data stream learning for emergency decision-making under uncertaintyPhysica A: Statistical Mechanics and its Applications10.1016/j.physa.2023.129429633(129429)Online publication date: Jan-2024
  • (2024)Cost-aware retraining for machine learningKnowledge-Based Systems10.1016/j.knosys.2024.111610293:COnline publication date: 7-Jun-2024
  • (2024)Cross-domain continual learning via CLAMPInformation Sciences: an International Journal10.1016/j.ins.2024.120813676:COnline publication date: 1-Aug-2024
  • (2024)Unsupervised domain adaptation by incremental learning for concept drifting data streamsInternational Journal of Machine Learning and Cybernetics10.1007/s13042-024-02135-115:9(4055-4078)Online publication date: 16-Apr-2024
  • (2024)Transfer learning for concept drifting data streams in heterogeneous environmentsKnowledge and Information Systems10.1007/s10115-023-02043-w66:5(2799-2857)Online publication date: 18-Jan-2024
  • (2023)Multimodal Batch-Wise Change DetectionIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2023.329484634:10(6783-6797)Online publication date: Oct-2023
  • (2023)Autonomous Cross Domain Adaptation Under Extreme Label ScarcityIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2022.318335634:10(6839-6850)Online publication date: Oct-2023
  • Show More Cited By

View Options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Login options

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media