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A review of multi-instance learning assumptions

  • James Foulds (a1) and Eibe Frank (a1)

Abstract

Multi-instance (MI) learning is a variant of inductive machine learning, where each learning example contains a bag of instances instead of a single feature vector. The term commonly refers to the supervised setting, where each bag is associated with a label. This type of representation is a natural fit for a number of real-world learning scenarios, including drug activity prediction and image classification, hence many MI learning algorithms have been proposed. Any MI learning method must relate instances to bag-level class labels, but many types of relationships between instances and class labels are possible. Although all early work in MI learning assumes a specific MI concept class known to be appropriate for a drug activity prediction domain; this ‘standard MI assumption’ is not guaranteed to hold in other domains. Much of the recent work in MI learning has concentrated on a relaxed view of the MI problem, where the standard MI assumption is dropped, and alternative assumptions are considered instead. However, often it is not clearly stated what particular assumption is used and how it relates to other assumptions that have been proposed. In this paper, we aim to clarify the use of alternative MI assumptions by reviewing the work done in this area.

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Amar, R., Dooly, D., Goldman, S., Zhang, Q. 2001. Multiple-instance learning of real-valued data. Proceedings of the 18th International Conference on Machine Learning, 310. ACM.
Andrews, S., Tsochantaridis, I., Hofmann, T. 2002. Support vector machines for multiple-instance learning. In Proceedings of the 16th Conference on Neural Information Processing Systems (Advances in Neural Information Processing Systems 15) 561568. MIT Press.
Auer, P., Ortner, R. 2004. A boosting approach to multiple instance learning. In Proceedings of the 15th European Conference on Machine Learning, 6374. Springer.
Blockeel, H., Page, D., Srinivasan, A. 2005. Multi-instance tree learning. In Proceedings of the 22nd International Conference on Machine Learning, 5764. ACM.
Burl, M. C., Weber, M., Perona, P. 1998. A probabilistic approach to object recognition using local photometry and global geometry. In Proceedings of the 5th European Conference on Computer Vision, 628641. Springer.
Chen, Y., Bi, J., Wang, J. Z. 2006. MILES: Multiple-instance learning via embedded instance selection. IEEE Transactions on Pattern Analysis and Machine Intelligence 28(12), 19311947.
Chen, Y., Wang, J. Z. 2004. Image categorization by learning and reasoning with regions. Journal of Machine Learning Research 5, 913939.
Cheung, P., Kwok, J. 2006. A regularization framework for multiple-instance learning. In Proceedings of the 23rd International Conference on Machine Learning, 193200. ACM.
Chevaleyre, Y., Zucker, J.-D. 2001. Solving multiple-instance and multiple-part learning problems with decision trees and rule sets. Application to the mutagenesis problem. In Proceedings of the 14th Biennial Conference of the Canadian Society for Computational Studies of Intelligence, 204214. Springer.
Dietterich, T. G., Lathrop, R. H., Lozano-Pérez, T. 1997. Solving the multiple instance problem with axis-parallel rectangles. Artificial Intelligence 89(1–2), 3171.
Dong, L. 2006. A comparison of multi-instance learning algorithms. Master’s thesis, University of Waikato.
Dooly, D., Zhang, Q., Goldman, S., Amar, R. 2002. Multiple-instance learning of real-valued data. Journal of Machine Learning Research 3, 651678.
Edgar, G. A. 1990. Measure, Topology, and Fractal Geometry, 2nd edn. Undergraduate Texts in Mathematics. Springer.
El-Manzalawy, Y., Honavar, V. 2007. MICCLLR: A generalized multiple-instance learning algorithm using class conditional log likelihood ratio. Technical report, Computer Science Department, Iowa State University.
Foulds, J. 2008. Learning Instance Weights in Multi-Instance Learning. Master’s thesis, University of Waikato.
Frank, E., Xu, X. 2003. Applying propositional learning algorithms to multi-instance data. Technical report 06/03, Department of Computer Science, University of Waikato.
Gärtner, T. 2000. Kernel-based Feature Space Transformation in Inductive Logic Programming. Master’s thesis, University of Bristol.
Gärtner, T., Flach, P. A., Kowalczyk, A., Smola, A. 2002. Multi-instance kernels. In Proceedings of the 19th International Conference on Machine Learning, 179186. Morgan Kaufmann.
Kriegel, H., Pryakhin, A., Schubert, M. 2006. An EM-approach for clustering multi-instance objects. In Proceedings of the 10th Pacific-Asia Conference on Knowledge Discovery and Data Mining, 139148. Springer.
Krogel, M.-A., Wrobel, S. 2002. Feature selection for propositionalization. In Proceedings of the 5th International Conference on Discovery Science, 430434. Springer.
Kück, H., de Freitas, N. 2005. Learning about individuals from group statistics. In Proceedings of the 21th Annual Conference on Uncertainty in Artificial Intelligence, 332339. AUAI Press.
Littlestone, N. 1987. Learning quickly when irrelevant attributes abound: A new linear-threshold algorithm. Machine Learning 2(4), 285318.
Maron, O. 1998. Learning from ambiguity. Ph.D. thesis, Massachusetts Institute of Technology.
Maron, O., Lozano-Pérez, T. 1997. A framework for multiple-instance learning. In Proceedings of the 11th Conference on Neural Information Processing Systems, 570576. MIT Press.
Maron, O., Ratan, A. L. 1998. Multiple-instance learning for natural scene classification. In Proceedings of the 15th International Conference on Machine Learning, 341349. Morgan Kaufmann.
Neuhaus, M., Bunke, H. 2007. A quadratic programming approach to the graph edit distance problem. In Proceedings of the 6th IAPR-TC-15 International Workshop on Graph Based Representations in Pattern Recognition, 92102. Springer.
Qi, G.-J., Hua, X.-S., Rui, Y., Mei, T., Tang, J., Zhang, H.-J. 2007. Concurrent multiple instance learning for image categorization. In Proceeding of the IEEE Conference on Computer Vision and Pattern Recognition, 18. IEEE Computer Society.
Ramon, J., De Raedt, L. 2000. Multi instance neural networks. In Proceedings of the International Conference on Machine Learning 2000 Workshop on Attribute-Value and Relational Learning.
Ray, S., Craven, M. 2005. Supervised learning versus multiple instance learning: an empirical comparison. In Proceedings of the 22nd International Conference on Machine Learning, 697704. ACM.
Ray, S., Page, D. 2001. Multiple instance regression. In Proceedings of the 18th International Conference on Machine Learning, 425432. Morgan Kaufmann.
Ruffo, G. 2000. Learning single and multiple instance decision trees for computer security applications. PhD thesis, Universida di Torino, Italy.
Schapire, R. E., Singer, Y. 2000. BoosTexter: A boosting-based system for text categorization. Machine Learning 39(2/3), 135168.
Scott, S., Zhang, J., Brown, J. 2005. On generalized multiple-instance learning. International Journal of Computational Intelligence and Applications 5(1), 2135.
Tao, Q., Scott, S. 2004. A faster algorithm for generalized multiple-instance learning. In Proceedings of the 17th International Florida Artificial Intelligence Research Society Conference, 550555. AAAI Press.
Tao, Q., Scott, S., Vinodchandran, N. V., Osugi, T., Mueller, B. 2004a. An extended kernel for generalized multiple-instance learning. In Proceedings of the 16th IEEE International Conference on Tools with Artificial Intelligence, 272277. IEEE Computer Society.
Tao, Q., Scott, S., Vinodchandran, N., Osugi, T. T. 2004b. SVM-based generalized multiple-instance learning via approximate box counting. In Proceedings of the 21st International Conference on Machine Learning, 779806. ACM.
Tsoumakas, G., Katakis, I. 2007. Multi-Label classification: An overview. International Journal of Data Warehousing and Mining 3(3), 113.
Wagstaff, K., Lane, T. 2007. Salience assignment for multiple-instance regression. In Proceedings of the International Conference on Machine Learning 2007 Workshop on Constrained Optimization and Structured Output Spaces.
Wang, J., Zucker, J.-D. 2000. Solving the multiple-instance problem: A lazy learning approach. In Proceedings of the 17th International Conference on Machine Learning, 11191125. Morgan Kaufmann.
Wang, Z., Radosavljevic, V., Han, B., Obradovic, Z. 2008. Aerosol optical depth prediction from satellite observations by multiple instance regression. In Proceedings of the SIAM International Conference on Data Mining, 165176. SIAM.
Weidmann, N. 2003. Two-level classification for generalized multi-instance data. Master’s thesis, Albert Ludwigs University of Freiburg.
Weidmann, N., Frank, E., Pfahringer, B. 2003. A two-level learning method for generalized multi-instance problems. In Proceedings of the 14th European Conference on Machine Learning, 468479. Springer.
Xu, X. 2003. Statistical Learning in Multiple Instance Problems. Master’s thesis, University of Waikato.
Xu, X., Frank, E. 2004. Logistic regression and boosting for labeled bags of instances. In Proceedings of the 8th Pacific-Asia Conference on Knowledge Discovery and Data Mining, 272281. Springer.
Zhang, M-L., Zhou, Z.-H. 2009. Multi-instance clustering with applications to multi-instance prediction. Applied Intelligence 31(1), 4768.
Zhang, Q., Goldman, S. 2001. EM-DD: An improved multiple-instance learning technique. In Proceedings of the 15th Conference on Neural Information Processing Systems, 10731080. MIT Press.
Zhang, Q., Yu, W., Goldman, S., Fritts, J. 2002. Content-based image retrieval using multiple-instance learning. In Proceedings of the 19th International Conference on Machine Learning, 682689. Morgan Kaufmann.
Zhou, Z.-H., Sun, Y.-Y., Li, Y.-F. 2009. Multi-instance learning by treating instances as non-I.I.D. samples. In Proceedings of the 26th International Conference on Machine Learning, 12491256. ACM.
Zhou, Z.-H., Xu, J.-M. 2007. On the relation between multi-instance learning and semi-supervised learning. In Proceedings of the 24th International Conference on Machine learning, 11671174. ACM.
Zhou, Z.-H., Zhang, M.-L. 2006. Multi-instance multi-label learning with application to scene classification. In Proceedings of the 20th Annual Conference on Neural Information Processing Systems, 16091616. MIT Press.
Zhou, Z.-H., Zhang, M.-L. 2007. Solving multi-instance problems with classifier ensemble based on constructive clustering. Knowledge and Information Systems 11(2), 155170.

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A review of multi-instance learning assumptions

  • James Foulds (a1) and Eibe Frank (a1)

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