Multiple-instance learning as a classifier combining problem

Pattern Recognition

Volumn 46, Issue 3, 2013, Pages 865-874

  Li, Yan ^a   Tax, David M J ^a   Duin, Robert P W ^a   Loog, Marco ^a,b  

^a Department of Microelectronics   (Netherlands)

^b UNIVERSITY OF COPENHAGEN   (Denmark)

Author keywords

Classifier combining; Multiple instance learning

Indexed keywords

BENCHMARK DATA; CLASSIFIER COMBINING; CONCEPT CLASS; FEATURE VECTORS; MIXTURE DISTRIBUTIONS; MULTIPLE INSTANCE LEARNING; OPTIMAL DECISION-RULE; SUPERVISED CLASSIFIERS; TOY DATA; TRAINING SETS; TWO PARAMETER;

PATTERN RECOGNITION;

SOFTWARE ENGINEERING;

EID: 84870251236     PISSN: 00313203     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patcog.2012.08.018     Document Type: Article

References (39)

1. L.A. Alexandre, A.C. Campilho, and M. Kamel On combining classifiers using sum and product rules Pattern Recognition Letters 22 12 2001 1283 1289
2. S. Ali, and M. Shah Human action recognition in videos using kinematic features and multiple instance learning IEEE Transactions on Pattern Analysis and Machine Intelligence 32 2 2010 288 303
3. S. Andrews, I. Tsochantaridis, T. Hofmann, Support vector machines for multiple-instance learning, in: Advances in Neural Information Processing Systems, 2003, pp. 577-584.
4. B. Babenko, M.H. Yang, S. Belongie, Visual tracking with online multiple instance learning, in: IEEE Conference on Computer Vision and Pattern Recognition, 2009, pp. 983-990.
5. B. Babenko, N. Verma, P. Dollar, S, Belongie, Multiple instance learning with manifold bags, in: Proceedings of the 28nd International Conference on Machine Learning, 2011, pp. 81-88.
6. C.M. Bishop Pattern Recognition and Machine Learning 2006 Springer New York
7. A. Blum, and A. Kalai A note on learning from multiple-instance examples Machine Learning 30 1 1998 23 29
8. Y. Chen, J. Bi, and J.Z. Wang MILES: multiple-instance learning via embedded instance selection IEEE Transactions on Pattern Analysis and Machine Intelligence 28 12 2006 1931 1947
9. T. Deselaers, V. Ferrari, A Conditional Random Field for Multiple-Instance Learning, in: Proceedings of the 27th International Conference on Machine Learning, 2010, pp. 287-294
10. T.G. Dietterich, R.H. Lathrop, and T. Lozano-Pérez Solving the multiple instance problem with axis-parallel rectangles Artificial Intelligence 89 1-2 1997 31 71
11. R. Duin, D. Tax, Experiments with classifier combining rules, in: Multiple Classifier Systems, 2000, pp. 16-29.
12. C. Elkan, K. Noto, Learning classifiers from only positive and unlabeled data, in: Proceedings of the 14th ACM Conference on Knowledge Discovery and Data Mining, 2008, pp. 213-220.
13. J. Foulds, and E. Frank A review of multi-instance learning assumptions Knowledge Engineering Review 25 1 2010 1 25
14. Z. Fu, A. Robles-Kelly, and J. Zhou MILIS: multiple instance learning with instance selection IEEE Transactions on Pattern Analysis and Machine Intelligence 33 5 2011 958 977
15. T. Gärtner, P.A. Flach, A. Kowalczyk, A.J. Smola, Multi-instance kernels, in: Proceedings of the 19th International Conference on Machine Learning, 2002, pp. 179-186.
16. P.V. Gehler, O. Chapelle, Deterministic annealing for multiple-instance learning, in: Proceedings of the 11th International Conference on AISTAT, 2007, pp. 123-130.
17. Y. Han, Q. Tao, J. Wang, Avoiding false positive in multi-instance learning, in: Advances in Neural Information Processing Systems, 2010, pp. 1-8.
18. Z. Jorgensen, Y. Zhou, and M. Inge A multiple instance learning strategy for combating good word attacks on spam filters Journal of Machine Learning Research 9 2008 1115 1146
19. M. Kim, F. De la Torre, Gaussian processes multiple-instance learning, in: Proceedings of the 27th International Conference on Machine Learning, 2010, pp. 535-542.
20. J. Kittler, M. Hatef, R.P.W. Duin, and J. Matas On combining classifiers IEEE Transactions on Pattern Analysis and Machine Intelligence 20 3 1998 226 239
21. C. Leistner, A. Saffari, H. Bischof, Miforests: multiple-instance learning with randomized trees, in: European Conference on Computer Vision, 2010, pp. 29-42.
22. F. Li, C. Sminchisescu, Convex Multiple-Instance Learning by Estimating Likelihood Ratio, in: Advances in Neural Information Processing Systems, 2010, pp. 1-8.
23. M. Loog, B. Van Ginneken, Static posterior probability fusion for signal detection: applications in the detection of interstitial diseases in chest radiographs, in: 17th International Conference on Pattern Recognition, vol. 1, 2004, pp. 644-647.
24. O.L. Mangasarian, and E.W. Wild Multiple instance classification via successive linear programming Journal of Optimization Theory and Applications 137 3 2008 555 568
25. O. Maron, T. Lozano-Pérez, A framework for multiple-instance learning, in: Advances in Neural Information Processing Systems, 1998, pp. 570-576.
26. J.F. Murray, G.F. Hughes, and K. Kreutz-Delgado Machine learning methods for predicting failures in hard drives a multiple-instance application Journal of Machine Learning Research 6 1 2006 783
27. S. Ray, M. Craven, Supervised versus multiple instance learning: an empirical comparison, in: Proceedings of the 22nd International Conference on Machine Learning, 2005, pp. 697-704.
28. L. Sorensen, M. Loog, D. Tax, W.J. Lee, M. de Bruijne, R. Duin, Dissimilarity-Based Multiple Instance Learning, in: S+SSPR, Lecture Notes in Computer Science, vol. 6218, 2010, pp. 129-138.
29. D.M.J. Tax, M. Van Breukelen, R.P.W. Duin, and J. Kittler Combining multiple classifiers by averaging or by multiplying? Pattern recognition 33 9 2000 1475 1485
30. P. Viola, J. Platt, C. Zhang, Multiple instance boosting for object detection, in: Advances in Neural Information Processing Systems, vol. 18, 2006, pp. 1417-1426.
31. H.Y. Wang, Q. Yang, H. Zha, Adaptive p-posterior mixture-model kernels for multiple instance learning, in: Proceedings of the 25th International Conference on Machine Learning, 2008, pp. 1136-1143.
32. J. Wang, J.D. Zucker, Solving the multiple-instance problem: a lazy learning approach, in: Proceedings of the 17th International Conference on Machine Learning, 2000, pp. 1119-1126.
33. N. Weidmann, E. Frank, B. Pfahringer, A two-level learning method for generalized multi-instance problems, in: Proceedings of the 14th European Conference on Machine Learning, 2003, pp. 468-479.
34. Y. Xie, Y. Qu, C. Li, and W. Zhang Online multiple instance gradient feature selection for robust visual tracking Pattern Recognition Letters 33 9 2012 1075 1082
35. X. Xu, E. Frank, Logistic regression and boosting for labeled bags of instances, in: PAKDD, Lecture Notes in Artificial Intelligence, vol. 3056, 2004.
36. B. Zeisl, C. Leistner, A. Saffari, H. Bischof, On-line semi-supervised multiple-instance boosting, in: IEEE Conference on Computer Vision and Pattern Recognition, IEEE, 2010, pp. 1879.
37. D. Zhang, Y. Liu, L. Si, J. Zhang, R.D. Lawrence, Multiple Instance Learning on Structured Data, in: Advances in Neural Information Processing Systems, vol. 24, 2011, pp. 145-153.
38. Q. Zhang, S.A. Goldman, EM-DD: an improved multiple-instance learning technique, in: Advances in Neural Information Processing Systems, vol. 2, 2002, pp. 1073-1080.
39. Z.H. Zhou, Y.Y. Sun, Y.F. Li, Multi-instance learning by treating instances as non-IID samples, in: Proceedings of the 26th International Conference on Machine Learning, 2009, pp. 1249-1256.