Abstract
Active Learning is a machine learning and data mining technique that selects the most informative samples for labeling and uses them as training data. It aims to obtain a high performance classifier by labeling as little data as possible from large amount of unlabeled samples, which means sampling strategy is the core issue. Existing approaches either tend to ignore information in unlabeled data and are prone to querying outliers or noise samples, or calculate large amounts of non-informative samples leading to significant computation cost. In order to solve above problems, this paper proposed a serial active learning framework. It first measures uncertainty of unlabeled samples and selects the most uncertain sample set. From which, it further generates the most representative sample set based on the mutual information criterion. Finally, the framework selects the most informative sample from the most representative sample set based on expected error reduction strategy. Experimental results on multiple datasets show that our approach outperforms Random Sampling and the state of the art adaptive active learning method.
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References
Abe, N., Zadrozny, B., Langford, J.: Outlier detection by active learning. In: KDD, pp. 504–509 (2006)
Koren, Y., Bell, R.: Advances in collaborative filtering. In: Recommender Systems Handbook, pp. 145–186. Springer (2011)
Rubens, N., Kaplan, D., Sugiyama, M.: Active learning in recommender systems. In: Recommender Systems Handbook, pp. 735–767 (2011)
Nguyen, H.T., Smeulders, A.W.M.: Active learning using pre-clustering. In: International Conference on Machine Learning, pp. 623–630 (2004)
Wang, Z., Ye, J.: Querying discriminative and representative samples for batch mode active learning. In: KDD, pp. 158–166 (2013)
Joshi, A.J., Porikli, F., Papanikolopoulos, N.P.: Scalable active learning for multiclass image classification. IEEE Trans. Pattern Anal. Mach. Intell. 34(11), 2259–2273 (2012)
Li, X., Guo, Y.: Adaptive active learning for image classification. In: CVPR, pp. 859–866 (2013)
Settles, B.: Active learning literature survey, vol. 52, pp. 55–66. University of Wisconsin, Madison (2010)
Fu, Y., Zhu, X., Li, B.: A survey on instance selection for active learning. Knowl. Inf. Syst. 35(2), 249–283 (2013)
Settles, B., Craven, M.: An analysis of active learning strategies for sequence labeling tasks. In: EMNLP, pp. 1070–1079 (2008)
Olsson, F.: A literature survey of active machine learning in the context of natural language processing. Sics technical report t2009:06, Swedish Institute of Computer Science (2009)
Fujino, A., Ueda, N., Nagata, M.: Adaptive semi-supervised learning on labeled and unlabeled data with different distributions. Knowledge and Information Systems 37(1), 129–154 (2013)
Bouguelia, M.R., Belaïd, Y., Belaïd, A.: A stream-based semi-supervised active learning approach for document classification. In: ICDAR, pp. 611–615 (2013)
Tuia, D., Ratle, F., Pacifici, F., Kanevski, M.F., Emery, W.J.: Active learning methods for remote sensing image classification. IEEE Transactions on Geoscience and Remote Sensing 47(7), 2218–2232 (2009)
Batina, L., Gierlichs, B., Prouff, E., Rivain, M., Standaert, F.X., Veyrat-Charvillon, N.: Mutual information analysis: a comprehensive study. Journal of Cryptology 24(2), 269–291 (2011)
Kapoor, A., Grauman, K., Urtasun, R., Darrell, T.: Active learning with gaussian processes for object categorization. In: ICCV 2007, pp. 1–8. IEEE (2007)
Rasmussen, C.E., Nickisch, H.: Gaussian processes for machine learning (gpml) toolbox. The Journal of Machine Learning Research 9999, 3011–3015 (2010)
Frank, A., Asuncion, A.: Uci machine learning repository (2010), http://archive.ics.uci.edu/ml
Li, F.F., Perona, P.: A bayesian hierarchical model for learning natural scene categories. In: CVPR, pp. 524–531 (2005)
Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines (2001), Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm .
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Li, C., Zhao, P., Wu, J., Xu, H., Cui, Z. (2014). A Serial Sample Selection Framework for Active Learning. In: Luo, X., Yu, J.X., Li, Z. (eds) Advanced Data Mining and Applications. ADMA 2014. Lecture Notes in Computer Science(), vol 8933. Springer, Cham. https://doi.org/10.1007/978-3-319-14717-8_34
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DOI: https://doi.org/10.1007/978-3-319-14717-8_34
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