Abstract
Regression is a popular machine learning task that aims to predict a numerical outcome. In multi-instance regression (MIR), each observation can be described by several instances. After a brief introduction to this topic in Sect. 6.1, we present a formal definition of MIR and its appropriate evaluation measures in Sect. 6.2. We organize the MIR methods in two main categories. Algorithms that focus on individual instances of each bag in their construction of a regression model are examined in Sect. 6.3, while Sect. 6.4 discusses methods that treat bags as single entities to create a regression model operating at the bag level. Section 6.5 lists some summarizing remarks.
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References
Amar, R., Dooly, D., Goldman, S., Zhang, Q.: Multiple-instance learning of real-valued data. In: Brodley, C., Danyluk, A. (eds.) Proceedings of the 18th International Confernce on Machine Learning (ICML 2001), pp. 3–10. Morgan Kaufmann Publishers, San Francisco (2001)
Andrews, S., Tsochantaridis, I., Hofmann, T.: Support vector machines for multiple-instance learning. In: Becker, S., Thrun, S., Obermayer, K. (eds.) Advances in Neural Information Processing Systems, pp. 561–568. MIT press, Cambridge (2002)
Chen, Y., Bi, J., Wang, J.: MILES: Multiple-instance learning via embedded instance selection. IEEE Trans. Pattern Anal. Mach. Intell. 28, 1931–1947 (2006)
Cheung, P., Kwok, J.: A regularization framework for multiple-instance learning. In: Cohen, W., Moore, A. (eds.) Proceedings of the 23rd International Conference on Machine learning (ICML 2006), pp. 193–200. ACM, New York (2006)
EL-Manzalawy, Y., Dobbs, D., Honavar, V.: Predicting MHC-II Binding Affinity Using Multiple Instance Regression. IEEE/ACM Trans. Comput. Biol. Bioinf. 8(4), 1067–1079 (2011)
Friedman, J.H.: Greedy function approximation: a gradient boosting machine. Ann. Stat. 29(5), 1189–1232 (2001)
Gärtner, T., Flach, P.A., Kowalczyk, A., Smola, A.: Multi-instance kernels. In: Sammut, C., Hoffmann, A. (eds.) Proceedings of the 19th International Conference on Machine Learning (ICML 2002), pp. 179–186. Morgan Kaufmann Publishers, San Francisco (2002)
Lu, L., Bi, J., Wolf, M., Salganicoff, M.: Effective 3D object detection and regression using probabilistic segmentation features in CT images. In: Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2011), pp. 1049–1056. IEEE, Los Alamitos (2011)
Pappas, N., Popescu-Belis, A.: Explaining the stars: weighted multiple-instance learning for aspect-based sentiment analysis. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, pp. 455–466. The Associations for Computational Linguistics, Stroudsburg (2014)
Ray, S.: Learning from data with complex interactions and ambiguous labels. PhD Thesis, University of Wisconsin at Madison, United States of America (2005)
Ray, S., Craven, M.: Supervised versus multiple instance learning: an empirical comparison. In: De Raedt, L., Wrobel, S. (eds.) Proceedings of the 22nd International Conference on Machine Learning (ICML 2005), pp. 697–704. ACM, New York (2005)
Ray, S., Page, D.: Multiple instance regression. In: Brodley, C., Danyluk, A. (eds.) Proceedings of the 18th International Confernce on Machine Learning (ICML 2001), pp. 425–432. Morgan Kaufmann Publishers, San Francisco (2001)
Schölkopf, B., Smola, A.: Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. MIT press, Cambridge (2002)
Shevade, S., Keerthi, S., Bhattacharyya, C., Murthy, K.: Improvements to the SMO algorithm for SVM regression. IEEE Trans. Neural Netw. 11(5), 1188–1193 (2000)
Teramoto, R., Kashima, H.: Prediction of protein-ligand binding affinities using multiple instance learning. J Mol. Gr. Model. 29(3), 492–497 (2010)
Wagstaff, K., Lane, T.: Salience assignment for multiple-instance regression. In: Proceedings of the ICML 2007 Workshop on Constrained Optimization and Structured Output Spaces, Citeseer (2007)
Wagstaff, K.L., Lane, T., Roper, A.: Multiple-instance regression with structured data. In: Bonchi, F., Berendt, B., Giannotti, F., Gunopulos, D., Turini, F., Zaniolo, C., Ramakrishnan, N., Wu, X. (eds.) Proceedings of the 2008 IEEE International Conference on Data Mining Workshops (ICDMW 08), pp. 291–300. IEEE, Los Alamitos (2008)
Wang, J., Zucker, J.: Solving the Multiple-Instance Problem: a Lazy Learning Approach. In: Langley, P. (ed.) Proceedings of the 17th International Conference on Machine Learning (ICML 2000), pp. 1119–1126. Morgan Kaufmann Publishers, San Francisco (2000)
Wang, Z., Lan, L., Vucetic, S.: Mixture model for multiple instance regression and applications in remote sensing. IEEE Trans. Geosci. Remote Sens. 50(6), 2226–2237 (2012)
Wang, Z., Radosavljevic, V., Han, B., Obradovic, Z., Vucetic, S.: Aerosol optical depth prediction from satellite observations by multiple instance regression. In: Apte, C., Park, H., Wang, K., Zaki, M. (eds.) Proceedings of the 2008 SIAM International Conference on Data Mining, pp. 165–176. SIAM, Philadelphia (2008)
Zhang, M., Zhou, Z.: Multi-instance clustering with applications to multi-instance prediction. Appl. Intell. 31(1), 47–68 (2009)
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Herrera, F. et al. (2016). Multi-instance Regression. In: Multiple Instance Learning. Springer, Cham. https://doi.org/10.1007/978-3-319-47759-6_6
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DOI: https://doi.org/10.1007/978-3-319-47759-6_6
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