Google Scholar

##plugins.themes.academic_pro.article.sidebar##

Published
Apr 11, 2015
Download
PDF
Statistic
Read Counter : 36 Download : 26

Downloads

Download data is not yet available.

##plugins.themes.academic_pro.article.main##

Abstract

Machine Learning has becoming an emerging topic within data mining. The active learning is also an upcoming topic. Active learning is the process of labeling unlabeled data instances by using queries and the labeling process will be done by expert labelers such as an oracle. The process of labeling will be very expensive and time consuming. The proposed method called a pairwise homogeneity active learning method which is an unsupervised label refinement method by using a pairwise homogeneity between the pair of data instances which improves the quality of the label. In this method we use a non expert labeler to provide the class label for data instances. The experimental results shows that the proposed method improves the labeling quality of the data instances which are being labeled.

Keywords: active learning, data mining, labeling, pairwise homogeneity, data instances.

##plugins.themes.academic_pro.article.details##

Author Biographies

Sneha Mary Thomas, Mangalam College of Engineering, Ettumanoor, Keral

Student, Department of Computer Science and Engineering

Nimmymol Manuel, Mangalam College of Engineering, Ettumanoor, Keral

Assistant Professor, Department of Computer Science and Engineering

How to Cite
Thomas, S. M., & Manuel, N. (2015). A Pairwise Homogeneity Active Learning. International Journal of Emerging Trends in Science and Technology, 2(04). Retrieved from http://igmpublication.org/ijetst.in/index.php/ijetst/article/view/612

References

1. Yifan Fui, Bin Li, Zingquan Zhu, “Active Learning without knowing individual instance labels:A Pairwise Label Homogeneity Query Approach”,IEEE Trans.on Knowledge and Data Engineering,vol.26, no.4, April 2014
2. Dayong Wang, Steven C.H. Hoi, Ying He and Jianke Zhu, “Mining weakly labeled web facial images for search-based face annotation”, IEEE Trans.on Knowledge and Data Engineering, vol.26,no.1,pp.13-64, Jan.2014
3. Y. Fu, B. Li, X. Zhu, and C. Zhang, “Do They Belong to the Same Class: Active Learning by Querying Pairwise Label Homogeneity”, Proc. 20th ACM Int’l Conf. Information and Knowledge Management (CIKM), pp. 2161-2164, 2011.
4. Y. Fu, X. Zhu, and B. Li, “A Survey on Instance Selection for Active Learning,” Knowledge and Information Systems, vol. 35, pp. 249-283, 2013.
5. H. Abe and H. Mamitsuka, “Query Learning Strategies Using Boosting and Bagging”, Proc. Int’l Conf. Machine Learning (ICML ’98), pp. 1-9, 1998
6. D.Cohn, Z. Ghahramani and M. Jordan, “Active Learning with Statistical Models”, J. Artificial Intelligence Research, vol. 4, pp. 129 145, 1996
7. A. Blum and S. Chawla, “Learning from Labeled and Unlabeled Data Using Graph Mincuts”, Proc. 18th Int’l Conf. Machine Learning (ICML), pp. 19-26, 2001
8. B. Settles, “Active Learning Literature Survey,” Technical Report 1648, 2009.
9. M. Fang, J. Yin, and X. Zhu, “Knowledge Transfer for Multi- Labeler Active Learning”, Proc. European Conf. Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD), Sept. 2013.
10. V.S. Sheng, F. Provost, and P.G. Ipeirotis, “Get Another Label? Improving Data Quality and Data Mining Using Multiple, Noisy Labelers,” Proc. 14th ACM SIGKDD Int’l Conf. Knowledge Discovery and Data Mining (KDD), 2008.