Abstract
In recent years, stream-based active learning has become an intensively investigated research topic. In this work, we propose a new algorithm for stream-based active learning that decides immediately whether to acquire a label (selective sampling). To this purpose, we extend our pool-based Probabilistic Active Learning framework into a framework for streams. In particular, we complement the notion of usefulness within a topological space (“spatial usefulness”) with the concept of “temporal usefulness”. To actively select the instances, for which labels must be acquired, we introduce the Balanced Incremental Quantile Filter (BIQF), an algorithm that assesses the usefulness of instances in a sliding window, ensuring that the predefined budget restrictions will be met within a given tolerance window. We compare our approach to other active learning approaches for streams and show the competitiveness of our method.
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Notes
- 1.
Companion website: http://kmd.cs.ovgu.de/res/pals.
- 2.
More learning curves are available on http://kmd.cs.ovgu.de/res/pals.
References
Arasu, A., Manku, G.S.: Approximate counts and quantiles over sliding windows. In: 23rd ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems, pp. 286–296. ACM, New York (2004)
Asuncion, A., Newman, D.J.: UCI Machine Learning Repository (2007)
Chapelle, O.: Active learning for parzen window classifier. In: International Workshop on Artificial Intelligence and Statistics, pp. 49–56 (2005)
Cheng, Y., Chen, Z., Liu, L., Wang, J., Agrawal, A., Choudhary, A.: Feedback-driven multiclass active learning for data streams. In: Proceedings of the 22nd ACM International Conference on Information & Knowledge Management, CIKM 2013, San Francisco, California, USA, pp. 1311–1320. ACM, New York (2013). doi:10.1145/2505515.2505528
Chu, W., Zinkevich, M., Li, L., Thomas, A., Tseng, B.: Unbiased online active learning in data streams. In: 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Diego, California, USA (2011)
Comer, D.: Ubiquitous b-tree. ACM Comput. Surv. 11(2), 121–137 (1979)
Freund, Y., Seung, H.S., Shamir, E., Tishby, N.: Selective sampling using the query by committee algorithm. Mach. Learn. 28(2–3), 133–168 (1997)
Halchenko, Y.O., Hanke, M.: Open is not enough. Let’s take the next step: an integrated, community-driven computing platform for neuroscience. Front. Neuroinf. 6, 22 (2012)
Harries, M.B., Sammut, C., Horn, K.: Extracting hidden context. Mach. Learn. 32, 101–126 (1998)
Huang, S., Dong, Y.: An active learning system for mining time-changing data streams. Intell. Data Anal. 11, 401–419 (2007)
Ienco, D., Bifet, A., Žliobaitė, I., Pfahringer, B.: Clustering based active learning for evolving data streams. In: Fürnkranz, J., Hüllermeier, E., Higuchi, T. (eds.) DS 2013. LNCS, vol. 8140, pp. 79–93. Springer, Heidelberg (2013)
Krempl, G., Ha, C.T., Spiliopoulou, M.: Clustering-based optimised probabilistic active learning (copal). In: 18th International Conference on Discovery Science (DS), Banff (2015)
Krempl, G., Kottke, D., Spiliopoulou, M.: Probabilistic active learning: towards combining versatility, optimality and efficiency. In: Džeroski, S., Panov, P., Kocev, D., Todorovski, L. (eds.) DS 2014. LNCS, vol. 8777, pp. 168–179. Springer, Heidelberg (2014)
Krempl, G., Zliobaite, I., Brzezinski, D., Hllermeier, E., Last, M., Lemaire, V., Noack, T., Shaker, A., Sievi, S., Spiliopoulou, M., Stefanowski, J.: Open challenges for data stream mining research. SIGKDD Explor. 16(1), 1–10 (2014)
Lewis, D.D., Gale, W.A.: A sequential algorithm for training text classifiers. In: 17th Annual Intenational ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 1–10 (1994)
Lindstrom, P., Delany, S.J., Namee, B.M.: Handling concept drift in a text data stream constrained by high labelling cost. In: FLAIRS Conference (2010)
Ng, A.Y., Jordan, M.I.: On discriminative vs. generative classifiers: a comparison of logistic regression and naive bayes. In: Advances in Neural Information Processing Systems 14, pp. 841–848. MIT Press (2002)
Roy, N., McCallum, A.: Toward optimal active learning through sampling estimation of error reduction. In: International Conference on Machine Learning, ICML 2001, pp. 441–448. Morgan Kaufmann Publishers Inc., San Francisco (2001)
Ryu, J.W., Kantardzic, M.M., Kim, M.-W., Ra Khil, A.: An efficient method of building an ensemble of classifiers in streaming data. In: Srinivasa, S., Bhatnagar, V. (eds.) BDA 2012. LNCS, vol. 7678, pp. 122–133. Springer, Heidelberg (2012)
Settles, B.: Active Learning Literature Survey. University of Wisconsin, Madison (2010)
Settles, B.: Active Learning. Synthesis Lectures on Artificial Intelligence and Machine Learning, vol. 6, no. 1, pp. 1–114 (2012)
Tomanek, K., Olsson, F.: A web survey on the use of active learning to support annotation of text data. In: NAACL HLT Workshop on Active Learning for Natural Language Processing, Stroudsburg, PA, USA, pp. 45–48 (2009)
Wang, L., Luo, G., Yi, K., Cormode, G.: Quantiles over data streams: an experimental study. In: Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data, SIGMOD 2013, pp. 737–748. ACM, New York (2013)
Wang, P., Zhang, P., Guo, L.: Mining multi-label data streams using ensemble-based active learning. In: SIAM Conference on Data Mining, pp. 1131–1140 (2012)
Zhu, X., Zhang, P., Lin, X., Shi, Y.: Active learning from stream data using optimal weight classifier ensemble. IEEE Trans. Syst. Man Cybern. Part B Cybern. 40(6), 1607–1621 (2010)
Zliobaite, I., Bifet, A., Pfahringer, B., Holmes, G.: Active learning with drifting streaming data. IEEE Trans. Neural Netw. Learn. Syst. 25(1), 27–39 (2014)
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Kottke, D., Krempl, G., Spiliopoulou, M. (2015). Probabilistic Active Learning in Datastreams. In: Fromont, E., De Bie, T., van Leeuwen, M. (eds) Advances in Intelligent Data Analysis XIV. IDA 2015. Lecture Notes in Computer Science(), vol 9385. Springer, Cham. https://doi.org/10.1007/978-3-319-24465-5_13
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