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Multiple Instance Learning

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Multiple Instance Learning

Abstract

This chapter provides a general introduction to the main subject matter of this work: multiple instance or multi-instance learning. The two terms are used interchangeably in the literature and they both convey the crucial point of difference with traditional (single-instance) learning. A formal description of multiple instance learning is provided in Sect. 2.1 and we discuss its origins in Sect. 2.2. In Sect. 2.3, we describe different learning tasks within this domain, which may or may not have an equivalent in single-instance learning. Finally, Sect. 2.4 lists a wide variety of applications corresponding to the different multi-instance learning paradigms.

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References

  1. Ali, K., Saenko, K.: Confidence-rated multiple instance boosting for object detection. In: Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2014), pp. 2433–2440. IEEE, Los Alamitos (2014)

    Google Scholar 

  2. Amar, R.A., Dooly, D.R., Goldman, S.A., Zhang, Q.: Multiple-instance learning of real-valued data. In: Brodley, C.E., Danyluk, A. (eds.) Proceedings of the 18th International Conference on Machine Learning (ICML 2001), pp. 3–10. Morgan Kaufmann Publishers, San Francisco (2001)

    Google Scholar 

  3. 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, vol. 15, pp. 561–568. MIT press, Cambridge (2002)

    Google Scholar 

  4. Babenko, B., Yang, M.H., Belongie, S.: Robust object tracking with online multiple instance learning. IEEE Trans. Pattern Anal. 33(8), 1619–1632 (2011)

    Article  Google Scholar 

  5. Blaschke, C., Leon, E., Krallinger, M., Valencia, A.: Evaluation of BioCreAtIvE assessment of task 2. BMC Bioinform. 6(1), 1 (2005)

    Article  Google Scholar 

  6. Braddock, P., Hu, D., Fan, T., Stratford, I., Harris, A., Bicknell, R.: A structure-activity analysis of antagonism of the growth factor and angiogenic activity of basic fibroblast growth factor by suramin and related polyanions. Br. J. Cancer 69(5), 890 (1994)

    Article  Google Scholar 

  7. Carneiro, G., Chan, A.B., Moreno, P.J., Vasconcelos, N.: Supervised learning of semantic classes for image annotation and retrieval. IEEE Trans. Pattern Anal. 29(3), 394–410 (2007)

    Article  Google Scholar 

  8. Chang, K., Bowyer, K., Flynn, P.: An evaluation of multimodal 2d+3d face biometrics. IEEE Trans. Pattern Anal. 27(4), 619–624 (2005)

    Article  Google Scholar 

  9. Chen, Y., Wang, J.: Image categorization by learning and reasoning with regions. J. Mach. Learn. Res. 5, 913–939 (2004)

    MathSciNet  Google Scholar 

  10. Chen, Y., Bi, J., Wang, J.Z.: MILES: Multiple-instance learning via embedded instance selection. IEEE Trans. Pattern Anal. 28(12), 1931–1947 (2006)

    Article  Google Scholar 

  11. Cheplygina, V., Tax, D.: Characterizing multiple instance datasets. In: Feragen, A., Pelilo, M., Loog, M. (eds.) Similarity-Based Pattern Recognition, pp. 15–27. Springer, Switzerland (2015)

    Chapter  Google Scholar 

  12. Davis, J., Costa, V.S., Ray, S., Page, D.: An integrated approach to feature invention and model construction for drug activity prediction. In: Ghahramani, Z. (ed.) Proceedings of the 24th international conference on Machine learning (ICML 2007), pp. 217–224. ACM, New York (2007)

    Google Scholar 

  13. De Raedt, L.: Attribute-value learning versus inductive logic programming: the missing links. In: Page, D. (ed.) Inductive Logic Programming. Lecture Notes in Computer Science, vol. 1446, pp. 1–8. Springer, Berlin (1998)

    Chapter  Google Scholar 

  14. De Raedt, L.: Logical and Relational Learning. Springer Science & Business Media, Berlin (2008)

    Book  MATH  Google Scholar 

  15. Deroski, S.: Relational data mining. In: Maimon, O., Rokach, L. (eds.) Data Mining and Knowledge Discovery Handbook, pp. 887–911. Springer, New York (2009)

    Chapter  Google Scholar 

  16. Dietterich, T.G., Lathrop, R.H., Lozano-Perez, T.: Solving the multiple instance problem with axis-parallel rectangles. Artif. Intell. 89(1–2), 31–71 (1997)

    Article  MATH  Google Scholar 

  17. Dooly, D.R., Goldman, S.A., Kwek, S.S.: Real-valued multiple-instance learning with queries. J. Comput. Syst. Sci. 72(1), 1–15 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  18. El-Manzalawy, Y., Dobbs, D., Honavar, V.: Predicting MHC-II binding affinity using multiple instance regression. IEEE ACM Trans. Comput. Biol. 8(4), 1067–1079 (2011)

    Article  Google Scholar 

  19. Faltemier, T., Bowyer, K., Flynn, P.: Using a multi-instance enrollment representation to improve 3D face recognition. Comput. Vis. Image Underst. 112(2), 114–125 (2008)

    Article  Google Scholar 

  20. Feng, S., Xiong, W., Li, B., Lang, C., Huang, X.: Hierarchical sparse representation based multi-instance semi-supervised learning with application to image categorization. Signal Process. 94, 595–607 (2014)

    Article  Google Scholar 

  21. Fu, G., Nan, X., Liu, H., Patel, R.Y., Daga, P.R., Chen, Y., Wilkins, D.E., Doerksen, R.J.: Implementation of multiple-instance learning in drug activity prediction. BMC Bioinform. 13(15), 1 (2012)

    Google Scholar 

  22. Fung, G., Dundar, M., Krishnapuram, B., Rao, R.B.: Multiple instance learning for computer aided diagnosis. Adv. Neural Inf. 19, 425 (2007)

    Google Scholar 

  23. Goldman, S.A., Scott, S.D.: Multiple-instance learning of real-valued geometric patterns. Ann. Math. Artif. Intel. 39(3), 259–290 (2003)

    Article  MathSciNet  MATH  Google Scholar 

  24. Han, Y., Qi, X.: A complementary svms-based image annotation system. In: Proceedings of the 2005 IEEE International Conference on Image Processing (ICIP 2005), vol. 1, pp. 1185–1188. IEEE, Los Alamitos (2005)

    Google Scholar 

  25. Hong, R., Wang, M., Gao, Y., Tao, D., Li, X., Wu, X.: Image annotation by multiple-instance learning with discriminative feature mapping and selection. IEEE Trans. Cybern. 44(5), 669–680 (2014)

    Article  Google Scholar 

  26. Huang, P., Zhu, J.: Multi-instance learning for software quality estimation in object-oriented systems: a case study. J. Zhejiang Univ.-Sci. C 11(2), 130–138 (2010)

    Article  Google Scholar 

  27. Kotsiantis, S., Kanellopoulos, D., Tampakas, V.: Financial application of multi-instance learning: two greek case studies. J. Converg. Inf. Technol. 5(8), 42–53 (2010)

    Article  Google Scholar 

  28. Kriegel, H.P., Pryakhin, A., Schubert, M.: An EM-approach for clustering multi-instance objects. In: Ng, W., Kitsuregawa, M., Li, J., Chang, K. (eds.) Lecture Notes in Artificial Intelligence, pp. 139–148. Springer, Berlin (2006)

    Google Scholar 

  29. Li, Y.X., Ji, S., Kumar, S., Ye, J., Zhou, Z.H.: Drosophila gene expression pattern annotation through multi-instance multi-label learning. IEEE ACM Trans. Comput. Biol. 9(1), 98–112 (2012)

    Article  Google Scholar 

  30. Liu, G., Wu, J., Zhou, Z.: Key instance detection in multi-instance learning. In: Hoi, S., Buntine, W. (eds.) JMLR: Workshop and Conference Proceedings: Asian Conference on Machine Learning, pp. 253–268 (2012)

    Google Scholar 

  31. Liu, J., Lu, Y., Zhou, T.: Instance significance guided multiple instance boosting for robust visual tracking (2015). arXiv preprint. arXiv:1501.04378

  32. Manandhar, A., Morton, K.D., Collins, L.M., Torrione, P.A.: Multiple instance learning for landmine detection using ground penetrating radar. In: Harmon, R., Holloway, J., Broach, J. (eds.) Proceedings of SPIE, Detection and Sensing of Mines, Explosive Objects and Obscured Targets, pp. 721–835. SPIE, Bellingham (2012)

    Google Scholar 

  33. Maron, O., Lozano-Pérez, T.: A framework for multiple-instance learning. In: Jordan, M., Kearns, M., Solla, S. (eds.) Advances in Neural Information, vol. 10, pp. 570–576. MIT press, Cambridge (1998)

    Google Scholar 

  34. Maron, O., Ratan, A.L.: Multiple-instance learning for natural scene classification. In: Shavlik, J. (ed.) Proceedings of the 15th International Conference on Machine Learning (ICML 1998), vol. 98, pp. 341–349. Morgan Kaufmann Publishers, San Francisco (1998)

    Google Scholar 

  35. McGovern, A., Barto, A.G.: Automatic discovery of subgoals in reinforcement learning using diverse density. In: Brodley, C., Danyluk, A. (eds.) Proceedings of the 18th International Conference on Machine Learning (ICML 2001), pp. 361–368. Morgan Kaufmann Publishers, San Francisco (2001)

    Google Scholar 

  36. Minhas, A., ul Amir, F., Ben-Hur, A.: Multiple instance learning of calmodulin binding sites. Bioinformatics 28(18), i416–i422 (2012)

    Article  Google Scholar 

  37. Muggleton, S., De Raedt, L.: Inductive logic programming: theory and methods. J. Logic Program. 19, 629–679 (1994)

    Article  MathSciNet  MATH  Google Scholar 

  38. Murray, J., Hughes, G., Kreutz, K.: Machine learning methods for predicting failures in hard drives: a multiple-instance application. J. Mach. Learn. Res. 6, 783–816 (2005)

    MathSciNet  MATH  Google Scholar 

  39. Ni, B., Song, Z., Yan, S.: Web image mining towards universal age estimator. In: Proceedings of the 17th ACM international conference on Multimedia, pp. 85–94. ACM, New York (2009)

    Google Scholar 

  40. Pfeifer, N., Kohlbacher, O.: Multiple instance learning allows MHC class II epitope predictions across alleles. In: Crandall, K., Lagergren, J. (eds.) Algorithms in Bioinformatics, pp. 210–221. Springer, Berlin (2008)

    Chapter  Google Scholar 

  41. Popescu, M., Mahnot, A.: Early illness recognition using in-home monitoring sensors and multiple instance learning. Method. Inform. Med. 51(4), 359 (2012)

    Article  Google Scholar 

  42. Qi, X., Han, Y.: Incorporating multiple svms for automatic image annotation. Pattern Recogn. 40(2), 728–741 (2007)

    Article  MATH  Google Scholar 

  43. Qi, G.J., Hua, X.S., Rui, Y., Mei, T., Tang, J., Zhang, H.J.: Concurrent multiple instance learning for image categorization. In: Proceedings of the 2007 IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2007), pp. 1–8. IEEE, Los Alamitos (2007)

    Google Scholar 

  44. Rahmani, R., Goldman, S.A.: MISSL: Multiple-instance semi-supervised learning. In: Cohen, W., Moore, A. (eds.) Proceedings of the 23rd International Conference on Machine Learning (ICML 2006), pp. 705–712. ACM, New York (2006)

    Google Scholar 

  45. 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)

    Google Scholar 

  46. Ray, S., Page, D.: Multiple instance regression. In: Brodley, C., Danyluk, A. (eds.) Proceedings of the 18th International Conference on Machine Learning (ICML 2001), pp. 425–432. Morgan Kaufmann Publishers, San Francisco (2001)

    Google Scholar 

  47. Reutemann, P.: Development of a propositionalization toolbox. Master’s thesis, Albert Ludwigs University of Freiburg, Germany (2004)

    Google Scholar 

  48. Reutemann, P., Pfahringer, B., Frank, E.: A toolbox for learning from relational data with propositional and multi-instance learners. In: Webb, G., Yu, X. (eds.) Lecture Notes in Artificial Intelligence, pp. 421–434. Springer, Berlin (2005)

    Google Scholar 

  49. Rocchio, J.J.: Relevance feedback in information retrieval. In: Salton, G. (ed.) The SMART Retrieval System: Experiments in Automatic Document Processing, pp. 313–323. Prentice-Hall, Englewood Cliffs (1971)

    Google Scholar 

  50. Ruffo, G.: Learning single and multiple instance decision trees for computer security applications. Ph.D. thesis, Department of Computer Science, University of Turin, Turin, Italy (2000)

    Google Scholar 

  51. Sánchez Tarragó, D., Cornelis, C., Bello, R., Herrera, F.: A multi-instance learning wrapper based on the Rocchio classifier for web index recommendation. Knowl.-Based Syst. 59, 173–181 (2014)

    Google Scholar 

  52. Srinivasan, A., Muggleton, S., King, R.D., Sternberg, M.J.: Mutagenesis: ILP experiments in a non-determinate biological domain. In: Wrobel, S. (ed.) Proceedings of the 4th international workshop on inductive logic programming, vol. 237, pp. 217–232. Gesellschaft fr Mathematik und Datenverarbeitung MBH, Bonn (1994)

    Google Scholar 

  53. Sun, L., Lu, Y., Yang, K., Li, S.: ECG analysis using multiple instance learning for myocardial infarction detection. IEEE Trans. Bio-Med. Eng. 59(12), 3348–3356 (2012)

    Article  Google Scholar 

  54. Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction. MIT press, Cambridge (1998)

    Google Scholar 

  55. Tao, Q., Scott, S., Vinodchandran, N., Osugi, T.T.: Svm-based generalized multiple-instance learning via approximate box counting. In: Greiner, R., Schuurmans, D. (eds.) Proceedings of the 21st International Conference on Machine Learning (ICML 2004), p. 101. ACM, New York (2004)

    Google Scholar 

  56. Teramoto, R., Kashima, H.: Prediction of protein-ligand binding affinities using multiple instance learning. J Mol. Graph. Model. 29(3), 492–497 (2010)

    Article  Google Scholar 

  57. Wagstaff, K.L., Lane, T.: Salience assignment for multiple-instance regression. In: Proceedings of the ICML 2007 Workshop on Constrained Optimization and Structured Output Spaces. Citeseer (2007)

    Google Scholar 

  58. 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)

    Chapter  Google Scholar 

  59. Wang, C., Scott, S., Zhang, J., Tao, Q., Fomenko, D.E., Gladyshev, V.N.: A study in modeling low-conservation protein superfamilies. CSE Technical reports, p. 35 (2004)

    Google Scholar 

  60. Wang, S., McKenna, M.T., Nguyen, T.B., Burns, J.E., Petrick, N., Sahiner, B., Summers, R.M.: Seeing is believing: video classification for computed tomographic colonography using multiple-instance learning. IEEE Trans. Med. Imaging 31(5), 1141–1153 (2012)

    Article  Google Scholar 

  61. Wang, Q., Yuan, Y., Yan, P., Li, X.: Saliency detection by multiple-instance learning. IEEE Trans. Cybern. 43(2), 660–672 (2013)

    Article  Google Scholar 

  62. 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)

    Chapter  Google Scholar 

  63. Wang, Z., Lan, L., Vucetic, S.: Mixture model for multiple instance regression and applications in remote sensing. IEEE Trans. Geosci. Remote 50(6), 2226–2237 (2012)

    Article  Google Scholar 

  64. Wu, J., Zhu, X., Zhang, C., Yu, P.S.: Bag constrained structure pattern mining for multi-graph classification. IEEE Trans. Knowl. Data. Eng. 26(10), 2382–2396 (2014)

    Article  Google Scholar 

  65. Wu, J., Pan, S., Zhu, X., Cai, Z.: Boosting for multi-graph classification. IEEE Trans. Cybern. 45(3), 416–429 (2015)

    Article  Google Scholar 

  66. Yang, C., Lozano-Pérez, T.: Image database retrieval with multiple-instance learning techniques. In: Proceedings of the 16th International Conference on Data Engineering, pp. 233–243. IEEE, Los Alamitos (2000)

    Google Scholar 

  67. Zafra, A., Romero, C., Ventura, S., Herrera-Viedma, E.: Multi-instance genetic programming for web index recommendation. Expert Syst. Appl. 36(9), 11470–11479 (2009)

    Article  Google Scholar 

  68. Zafra, A., Romero, C., Ventura, S.: Multiple instance learning for classifying students in learning management systems. Expert Syst. Appl. 38(12), 15020–15031 (2011)

    Article  Google Scholar 

  69. Zafra, A., Gibaja, E.L., Ventura, S.: Multiple instance learning with multiple objective genetic programming for web mining. Appl. Soft Comput. 11(1), 93–102 (2011)

    Article  Google Scholar 

  70. Zafra, A., Ventura, S.: Multi-instance genetic programming for predicting student performance in web based educational environments. Appl. Soft Comput. 12(8), 2693–2706 (2012)

    Article  Google Scholar 

  71. Zhang, C., Chen, X.: Region-based image clustering and retrieval using multiple instance learning. In: Leow, W., Lew, M., Chua, T., Ma, W., Chaisom, L., Bakker, E. (eds.) Lecture Notes in Computer Science, pp. 194–204. Springer, Berlin (2005)

    Google Scholar 

  72. Zhang, Q., Goldman, S.A.: EM-DD: an improved multiple-instance learning technique. In: Dietterich, T., Becker, S., Ghahramani, Z (eds.) Advances in Neural Information, pp. 1073–1080. MIT press, Cambridge (2001)

    Google Scholar 

  73. Zhang, K., Song, H.: Real-time visual tracking via online weighted multiple instance learning. Pattern Recogn. 46(1), 397–411 (2013)

    Article  MATH  Google Scholar 

  74. Zhang, M.L., Zhou, Z.H.: Multi-instance clustering with applications to multi-instance prediction. Appl. Intell. 31(1), 47–68 (2009)

    Article  Google Scholar 

  75. Zhang, Q., Goldman, S.A., Yu, W., Fritts, J.E.: Content-based image retrieval using multiple-instance learning. In: Sammut, C., Hoffman, A. (eds.) Proceedings of the 19th International Conference on Machine Learning (ICML 2002), pp. 682–689. Morgan Kaufmann Publishers, San Francisco (2002)

    Google Scholar 

  76. Zhang, C., Chen, S.C., Shyu, M.L.: Multiple object retrieval for image databases using multiple instance learning and relevance feedback. In: Proceedings of the 2004 IEEE International Conference on Multimedia and Expo (ICME 2004), vol. 2, pp. 775–778. IEEE, Los Alamitos (2004)

    Google Scholar 

  77. Zhang, C., Chen, X., Chen, M., Chen, S.C., Shyu, M.L.: A multiple instance learning approach for content based image retrieval using one-class support vector machine. In: Proceedings of the 2005 IEEE International Conference on Multimedia and Expo (ICME 2005), pp. 1142–1145. IEEE, Los Alamitos (2005)

    Google Scholar 

  78. Zhang, D., Wang, F., Si, L., Li, T.: M3IC: maximum margin multiple instance clustering. In: Proceedings of the 21st International Joint Conference on Artificial Intelligence (IJCAI 2009), vol. 9, pp. 1339–1344 (2009)

    Google Scholar 

  79. Zhang, D., Wang, F., Si, L., Li, T.: Maximum margin multiple instance clustering with applications to image and text clustering. IEEE Trans. Neural Netw. 22(5), 739–751 (2011)

    Article  Google Scholar 

  80. Zhao, Z., Fu, G., Liu, S., Elokely, K.M., Doerksen, R.J., Chen, Y., Wilkins, D.E.: Drug activity prediction using multiple-instance learning via joint instance and feature selection. BMC Bioinform. 14(Suppl 14), S16 (2013)

    Article  Google Scholar 

  81. Zhou, Z., Jiang, K., Li, M.: Multi-instance learning based web mining. Appl. Intell. 22(2), 135–147 (2005)

    Article  Google Scholar 

  82. Zhou, Z.H., Zhang, M.L., Huang, S.J., Li, Y.F.: Multi-instance multi-label learning. Artif. Intell. 176(1), 2291–2320 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  83. Zhou, T., Lu, Y., Qiu, M.: Online visual tracking using multiple instance learning with instance significance estimation. Comput. Res. Repos. (2015)

    Google Scholar 

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Authors and Affiliations

  1. Department of Computer Science and Artificial Intelligence, University of Granada, Granada, Spain

    Francisco Herrera

  2. Department of Computer Science, University of Córdoba, Córdoba, Spain

    Sebastián Ventura

  3. Center of Information Studies, Central University “Marta Abreu” of Las Villas, Santa Clara, Villa Clara, Cuba

    Rafael Bello

  4. Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Ghent, Belgium

    Chris Cornelis & Sarah Vluymans

  5. Department of Computer Science and Numerical Analysis, University of Córdoba, Córdoba, Spain

    Amelia Zafra

  6. Central University “Marta Abreu” of Las Villas, Santa Clara, Villa Clara, Cuba

    Dánel Sánchez-Tarragó

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  1. Francisco Herrera
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  2. Sebastián Ventura
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Herrera, F. et al. (2016). Multiple Instance Learning. In: Multiple Instance Learning. Springer, Cham. https://doi.org/10.1007/978-3-319-47759-6_2

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