Abstract
By selecting and asking the user to label only the most informative instances, active learners can significantly reduce the number of labeled training instances to learn a classification function. We focus here on how to select the most informative instances for labeling. In this paper we make three contributions. First, in contrast to the leading sampling strategy of halving the volume of version space, we present the sampling strategy of reducing the volume of version space by more than half with the assumption of target function being chosen from nonuniform distribution over version space. Second, via Halving model, we propose the idea of sampling the instances that would be most possibly misclassified. Third, we present a sampling method named CBMPMS (Committee Based Most Possible Misclassification Sampling) which samples the instances that have the largest probability to be misclassified by the current classifier. Comparing the proposed CBMPMS method with the existing active learning methods, when the classifiers achieve the same accuracy, the former method will sample fewer times than the latter ones. The experiments show that the proposed method outperforms the traditional sampling methods on most selected datasets.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Lewis, D.D., Gale, W.A.: A sequential algorithm for training text classifiers. In: Proc. of SIGIR-94. 17th ACM International Conference on Research and Development in Information Retrieval, pp. 3–12. Springer, Heidelberg (1994)
Hieu, T., Arnold, S.: Active Learning Using Pre-clustering. In: Proc. 21th International Conf. on Machine Learning, Banff, Canada (2004)
Schein, A.I.: Active learning for logistic regression: [Ph D dissertation] (2004)
Tong, S., Koller, D.: Support vector machine active learning with applications to text classification. Journal of Machine Learning Research 2, 45–66 (2001)
Schohn, G., Cohn, D.: Less is more: Active learning with support vector machines. In: Proc. 17th International Conf on Machine Learning, pp. 839–846. Morgan Kaufmann, San Francisco (2000)
Campbell, C., Cristianini, N., Smola, A.: Query learning with large margin classifiers. In: Proc. 17th International Conf. on Machine Learning, pp. 111–118. Morgan Kaufmann, San Francisco (2000)
Cohn, D.A., Ghahramani, Z., Jordan, M.I.: Active learning with statistical models. Journal of Artificial Intelligence research 4, 129–145 (1996)
Roy, N., McCallum, A.: Toward optimal active learning through sampling estimation of error reduction. In: Proc. 18th International Conf. on Machine Learning, pp. 441–448. Morgan Kaufmann, San Francisco, CA (2001)
Seung, H.S., Opper, M., Sompolinsky, H.: Query by committee. Computational Learning Theory, 287–294 (1992)
Freund, Y., Seung, H.S., Shamir, E., Tishby, N.: Selective sampling using the query by committee algorithm. Machine Learning 28, 133–168 (1997)
Cohn, D.A., Ladner, R.: Improving generalization with active learning. Machine Learning 15, 201–221 (1994)
Abe, N., Mamitsuka, H.: Query learning using boosting and bagging. In: Proc. 15th International Conf on Machine Learning, pp. 1–10. Morgan Kaufmann, Madison, CA (1998)
Melville, P., Mooney, R.J.: Diverse ensembles for active learning. In: Proc. 21th International Conf. on Machine Learning, pp. 584–591. Morgan Kaufmann, Banff, CA (2004)
Hansen, L., Salamon, P.: Neural network ensembles. IEEE Trans. Pattern Analysis and Machine Intell. 12, 993–1001 (1990)
Newman, D.J., Hettich, S., Blake, C.L., Merz, C.J.: UCI Repository of machine learning databases. Irvine, CA: University of California, Department of Information and Computer Science (1998), http://www.ics.uci.edu/~mlearn/MLRepository.html
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Long, J., Yin, J., Zhu, E. (2007). An Active Learning Method Based on Most Possible Misclassification Sampling Using Committee. In: Torra, V., Narukawa, Y., Yoshida, Y. (eds) Modeling Decisions for Artificial Intelligence. MDAI 2007. Lecture Notes in Computer Science(), vol 4617. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73729-2_10
Download citation
DOI: https://doi.org/10.1007/978-3-540-73729-2_10
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-73728-5
Online ISBN: 978-3-540-73729-2
eBook Packages: Computer ScienceComputer Science (R0)