A self-training approach to cost sensitive uncertainty sampling

Machine Learning

Volumn 76, Issue 2-3, 2009, Pages 257-270

  Liu, Alexander ^a   Jun, Goo ^a   Ghosh, Joydeep ^a  

^a The University of Texas at Austin   (United States)

Author keywords

Active learning; Cost sensitive learning; Self training

Indexed keywords

ACTIVE LEARNING; ACTIVE LEARNING METHODS; COMPUTATIONALLY EFFICIENT; COST-SENSITIVE; COST-SENSITIVE LEARNING; MISCLASSIFICATION COSTS; POSTERIOR PROBABILITY; REDUCTION METHOD; SELF-TRAINING; STANDARD UNCERTAINTY;

COSTS; EDUCATION; LABELS; UNCERTAINTY ANALYSIS;

COST BENEFIT ANALYSIS;

EID: 68949155363     PISSN: 08856125     EISSN: 15730565     Source Type: Journal    
DOI: 10.1007/s10994-009-5131-9     Document Type: Conference Paper

References (25)

1. Ando, R., & Zhang, T. (2005). A high-performance semi-supervised learning method for text chunking. In Proceedings of the 43rd annual meeting on association for computational linguistics (pp. 1-9). NJ: Association for Computational Linguistics Morristown.
2. Asuncion, A., & Newman, D. (2007). UCI machine learning repository.
3. Beygelzimer, A., Dasgupta, S., & Langford, J. (2009). Importance weighted active learning. In ICML '09: Proceedings of the 26th international conference on Machine learning.
4. Chen, Y., Crawford, M., & Ghosh, J. (2007). Knowledge based stacking of hyperspectral data for land cover classification. In Computational intelligence and data mining, 2007. CIDM 2007. IEEE Symposium on (pp. 316-322).
5. Dasgupta, S., & Hsu, D. (2008). Hierarchical sampling for active learning. In ICML '08: Proceedings of the 25th international conference on Machine learning (pp. 208-215). Helsinki, Finland.
6. S. Dasgupta D. Hsu C. Monteleoni 2008 A general agnostic active learning algorithm Advances in Neural Information Processing Systems 20 353 360
7. Elkan, C. (2001). The foundations of cost-sensitive learning. In Proceedings of the seventeenth international joint conference on artificial intelligence (pp. 973-978).
8. J. Ham Y. Chen M. M. Crawford J. Ghosh 2005 Investigation of the random forest framework for classification of hyperspectral data IEEE Transactions on Geoscience and Remote Sensing 43 3 492 501
9. Karypis, G. (2002). CLUTO-a clustering toolkit. University of Minnesota technical report 02-017.
10. S. Kumar J. Ghosh M. M. Crawford 2001 Best-bases feature extraction algorithms for classification of hyperspectral data IEEE Transactions on Geoscience and Remote Sensing 39 7 1368 1379
11. D. Landgrebe 2002 Hyperspectral image data analysis Signal Processing Magazine, IEEE 19 17 28
12. Lewis, D. D., & Catlett, J. (1994). Heterogeneous uncertainty sampling for supervised learning. In Proceedings of the eleventh international conference on machine learning (pp. 148-156). San Mateo: Morgan Kaufmann.
13. Margineantu, D. D. (2005). Active cost-sensitive learning. In The nineteenth international joint conference on artificial intelligence. Edinburgh, Scotland.
14. McCallum, A., & Nigam, K. (1998). A comparison of event models for Naive Bayes text classification. In AAAI-98 workshop on learning for text categorization.
15. McClosky, D., Charniak, E., & Johnson, M. (2006). Effective self-training for parsing. In Proceedings of HLT-NAACL 2006.
16. Morgan, J. T. (2002). Adaptive hierarchical classifier with limited training data. PhD thesis, Univ. of Texas at Austin.
17. Rosenberg, C., Hebert, M., & Schneiderman, H. (2005). Semi-supervised self-training of object detection models. In Seventh IEEE workshop on applications of computer vision (vol. 1, pp. 29-36).
18. Roy, N., & McCallum, A. (2001). Toward optimal active learning through sampling estimation of error reduction. In Proceedings of the 18th international conference on machine learning (pp. 441-448). San Mateo: Morgan Kaufmann.
19. M. Saar-Tsechansky F. Provost 2004 Active sampling for class probability estimation and ranking Machine Learning 54 2 153 178
20. M. Saar-Tsechansky F. Provost 2007 Decision-centric active learning of binary-outcome models Information Systems Research 18 1 1 19
21. Settles, B. (2009). Active learning literature survey. Computer sciences technical report 1648, University of Wisconsin-Madison.
22. Seung, H. S., Opper, M., & Sompolinsky, H. (1992). Query by committee. In Proceedings of the fifth annual workshop on computational learning theory (pp. 287-294). Pittsburgh: ACM.
23. Yarowsky, D. (1995). Unsupervised word sense disambiguation rivaling supervised methods. In Proceedings of the 33rd annual meeting on association for computational linguistics (pp. 189-196).
24. Zhong, S., & Ghosh, J. (2003). A comparative study of generative models for document clustering. In SDM workshop on clustering high dimensional data and its applications.
25. Zhu, X. (2005). Semi-supervised learning literature survey. Tech. Rep. 1530, Computer Sciences, University of Wisconsin-Madison.