Skip to main content
SpringerLink
Log in

Multi-instance clustering with applications to multi-instance prediction

  • Published:
Applied Intelligence Aims and scope Submit manuscript

Abstract

In the setting of multi-instance learning, each object is represented by a bag composed of multiple instances instead of by a single instance in a traditional learning setting. Previous works in this area only concern multi-instance prediction problems where each bag is associated with a binary (classification) or real-valued (regression) label. However, unsupervised multi-instance learning where bags are without labels has not been studied. In this paper, the problem of unsupervised multi-instance learning is addressed where a multi-instance clustering algorithm named Bamic is proposed. Briefly, by regarding bags as atomic data items and using some form of distance metric to measure distances between bags, Bamic adapts the popular k -Medoids algorithm to partition the unlabeled training bags into k disjoint groups of bags. Furthermore, based on the clustering results, a novel multi-instance prediction algorithm named Bartmip is developed. Firstly, each bag is re-represented by a k-dimensional feature vector, where the value of the i-th feature is set to be the distance between the bag and the medoid of the i-th group. After that, bags are transformed into feature vectors so that common supervised learners are used to learn from the transformed feature vectors each associated with the original bag’s label. Extensive experiments show that Bamic could effectively discover the underlying structure of the data set and Bartmip works quite well on various kinds of multi-instance prediction problems.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Dietterich TG, Lathrop RH, Lozano-Pérez T (1997) Solving the multiple-instance problem with axis-parallel rectangles. Artif Intell 39(1-2):31–71

    Article  Google Scholar 

  2. Weidmann N, Frank E, Pfahringer B (2003) A two-level learning method for generalized multi-instance problem. In: Lavrač N, Gamberger D, Blockeel H, Todorovski L (eds) Lecture notes in artificial intelligence, vol 2837. Springer, Berlin, pp 468–579

    Google Scholar 

  3. Scott S, Zhang J, Brown J (2003) On generalized multiple-instance learning. Technical report UNL-CSE-2003-5, Department of Computer Science and Engineering, University of Nebraska, Lincoln, NE

  4. Amar RA, Dooly DR, Goldman SA, Zhang Q (2001) Multiple-instance learning of real-valued data. In: Proceedings of the 18th international conference on machine learning, Williamstown, MA, 2001, pp 3–10

  5. Ray S, Page D (2001) Multiple instance regression. In: Proceedings of the 18th international conference on machine learning, Williamstown, MA, 2001, pp 425–432

  6. Dooly DR, Zhang Q, Goldman SA, Amar RA (2002) Multiple-instance learning of real-valued data. J Mach Learn Res 3:651–678

    Article  Google Scholar 

  7. Han J, Kamber M (2001) Data mining: concepts and techniques. Morgan Kaufmann, San Francisco

    Google Scholar 

  8. Edgar GA (1995) Measure, Topology, and Fractal Geometry, 3rd edn. Springer, Berlin

    Google Scholar 

  9. Long PM, Tan L (1998) Pac learning axis-aligned rectangles with respect to product distribution from multiple-instance examples. Mach Learn 20(1):7–21

    Article  Google Scholar 

  10. Auer P, Long PM, Srinivasan A (1998) Approximating hyper-rectangles: learning and pseudo-random sets. J Comput Syst Sci 57(3):376–388

    Article  MATH  MathSciNet  Google Scholar 

  11. Auer P (1997) On learning from multi-instance examples: empirical evaluation of a theoretical approach. In: Proceedings of the 14th international conference on machine learning, Nashville, TN, 1997, pp 21–29

  12. Blum A, Kalai A (1998) A note on learning from multiple-instance examples. Mach Learn 30(1):23–29

    Article  MATH  Google Scholar 

  13. Goldman SA, Kwek SS, Scott SD (2001) Agnostic learning of geometric patterns. J Comput Syst Sci 62(1):123–151

    Article  MATH  MathSciNet  Google Scholar 

  14. Goldman SA, Scott SD (2003) Multiple-instance learning of real-valued geometric patterns. Ann Math Artif Intell 39(3):259–290

    Article  MATH  MathSciNet  Google Scholar 

  15. Maron O, Lozano-Pérez T (1998) A framework for multiple-instance learning. In: Jordan MI, Kearns MJ, Solla SA (eds) Advances in neural information processing systems, vol 10. MIT Press, Cambridge, pp 570–576

    Google Scholar 

  16. Zhang Q, Goldman SA (2002) Em-dd: an improved multiple-instance learning technique. In: Dietterich TG, Becker S, Ghahramani Z (eds) Advances in neural information processing systems, vol 14. MIT Press, Cambridge, pp 1073–1080

    Google Scholar 

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

    Google Scholar 

  18. 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, San Francisco, CA, 2000, pp 1119–1125

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

  20. Chevaleyre Y, Zucker J-D (2001) Solving multiple-instance and multiple-part learning problems with decision trees and decision rules. Application to the mutagenesis problem. In: Stroulia E, Matwin S (eds) Lecture notes in artificial intelligence, vol 2056. Springer, Berlin, pp 204–214

    Google Scholar 

  21. Blockeel H, Page D, Srinivasan A (2005) Multi-instance tree learning. In: Proceedings of the 22nd international conference on machine learning, Bonn, Germany, 2005, pp 57–64

  22. Xu X, Frank E (2004) Logistic regression and boosting for labeled bags of instances. In: Dai H, Srikant R, Zhang C-Q (eds) Lecture notes in computer science, vol 3056. Springer, Berlin, pp 272–281

    Google Scholar 

  23. Ramon J, De Raedt L (2000) Multi instance neural networks. In: Proceedings of ICML-2000, workshop on attribute-value and relational learning, San Francisco, CA, 2000

  24. Zhou Z-H, Zhang M-L (2002) Neural networks for multi-instance learning. Technical report, AI Lab, Computer Science and Technology Department, Nanjing University, Nanjing, China

  25. Zhang M-L, Zhou Z-H (2004) Improve multi-instance neural networks through feature selection. Neural Process Lett 19(1):1–10

    Article  MATH  Google Scholar 

  26. Bouchachia A (2004) Multiple instance learning with radial basis function neural networks. In: Pal NR, Kasabov N, Mudi RK (eds) Lecture notes in computer science, vol 3316. Springer, Berlin, pp 440–445

    Google Scholar 

  27. Zhang M-L, Zhou Z-H (2006) Adapting RBF neural networks to multi-instance learning. Neural Process Lett 23(1):1–26

    Article  Google Scholar 

  28. Gärtner T, Flach PA, Kowalczyk A, Smola AJ (2002) Multi-instance kernels. In: Proceedings of the 19th international conference on machine learning, Sydney, Australia, 2002, pp 179–186

  29. Andrews S, Tsochantaridis I, Hofmann T (2003) Support vector machines for multiple-instance learning. In: Becker S, Thrun S, Obermayer K (eds) Advances in neural information processing systems, vol 15. MIT Press, Cambridge, pp 561–568

    Google Scholar 

  30. Cheung P-M, Kwok JT (2006) A regularization framework for multiple-instance learning. In: Proceedings of the 23th international conference on machine learning, Pittsburgh, PE, 2006, pp 193–200

  31. Zhou Z-H, Zhang M-L (2003) Ensembles of multi-instance learners. In: Lavrač N, Gamberger D, Blockeel H, Todorovski L (eds) Lecture notes in artificial intelligence, vol 2837. Springer, Berlin, pp 492–502

    Google Scholar 

  32. Andrews S, Hofmann T (2004) Multiple-instance learning via disjunctive programming boosting. In: Thrun S, Saul LK, Schölkopf B (eds) Advances in neural information processing systems, vol 16. MIT Press, Cambridge

    Google Scholar 

  33. Auer P, Ortner R (2004) A boosting approach to multiple instance learning. In: Boulicaut J-F, Esposito F, Giannotti F, Pedreschi D (eds) Lecture notes in artificial intelligence, vol 3201. Springer, Berlin, pp 63–74

    Google Scholar 

  34. Viola P, Platt J, Zhang C (2006) Multiple instance boosting for object detection. In: Weiss Y, Schölkopf B, Platt J (eds) Advances in neural information processing systems, vol 18. MIT Press, Cambridge, pp 1419–1426

    Google Scholar 

  35. Huang X, Chen S-C, Shyu M-L, Zhang C (2002) Mining high-level user concepts with multiple instance learning and relevance feedback for content-based image retrieval. In: Zaïane OR, Simoff SJ, Djeraba C (eds) Lecture notes in artificial intelligence, vol 2797. Springer, Berlin, pp 50–67

    Google Scholar 

  36. Yang C, Lozano-Pérez T (2000) Image database retrieval with multiple-instance learning techniques. In: Proceedings of the 16th international conference on data engineering, San Diego, CA, 2000, pp 233–243

  37. Zhang Q, Yu W, Goldman SA, Fritts JE (2002) Content-based image retrieval using multiple-instance learning. In: Proceedings of the 19th international conference on machine learning, Sydney, Australia, 2002, pp 682–689

  38. Zhou Z-H, Zhang M-L, Chen K-J (2003) A novel bag generator for image database retrieval with multi-instance learning techniques. In: Proceedings of the 15th IEEE international conference on tools with artificial intelligence, Sacramento, CA, 2003, pp 565–569

  39. Rahmani R, Goldman SA (2006) Missl: multiple-instance semi-supervised learning. In: Proceedings of the 23rd international conference on machine learning, Pittsburgh, PE, 2006, pp 705–712

  40. Maron O, Ratan AL (1998) Multiple-instance learning for natural scene classification. In: Proceedings of the 15th international conference on machine learning, Madison, WI, 1998, pp 341–349

  41. McGovern A, Barto AG (2001) Automatic discovery of subgoals in reinforcement learning using diverse density. In: Proceedings of the 18th international conference on machine learning, Williamstown, MA, 2001, pp 361–368

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

    Article  Google Scholar 

  43. Zhou Z-H, Zhang M-L (2007) Solving multi-instance problems with classifier ensemble based on constructive clustering. Knowl Inf Syst 11(2):155–170

    Article  Google Scholar 

  44. 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, Miami Beach, FL, 2004, pp 550–555

  45. Tao Q, Scott S, Vinodchandran NV, Osugi TT (2004) Svm-based generalized multiple-instance learning via approximate box counting. In: Proceedings of the 21st international conference on machine learning, Banff, Canada, 2004, pp 799–806

  46. Tao Q, Scott S, Vinodchandran NV, Osugi TT, Mueller B (2004) An extended kernel for generalized multiple-instance learning. In: Proceedings of the 16th IEEE international conference on tools with artificial intelligence, Boca Raton, FL, 2004, pp 272–277

  47. De Raedt L (1998) Attribute-value learning versus inductive logic programming: the missing links. In: Page D (ed) Lecture notes in artificial intelligence, vol 1446. Springer, Berlin, pp 1–8

    Google Scholar 

  48. Alphonse É, Matwin S (2004) Filtering multi-instance problems to reduce dimensionality in relational learning. J Intell Inf Syst 22(1):23–40

    Article  Google Scholar 

  49. McGovern A, Jensen D (2003) Identifying predictive structures in relational data using multiple instance learning. In: Proceedings of the 20th international conference on machine learning, Washington, DC, 2003, pp 528–535

  50. Günter S, Bunke H (2003) Validation indices for graph clustering. Pattern Recogn Lett 24(8):1107–1113

    Article  MATH  Google Scholar 

  51. Quinlan JR (1993) C4.5: Programs for machine learning. Morgan Kaufmann, San Mateo

    Google Scholar 

  52. Blake C, Keogh E, Merz CJ (1998) UCI repository of machine learning databases [http://www.ics.uci.edu/~mlearn/mlrepository.html]. Technical report, Department of Information and Computer Science, University of California, Irvine, CA

  53. Berry RS, Rice SA, Ross J (1980) Physical Chemistry. Wiley, New York, Chapter 10, Intermolecular Forces

    Google Scholar 

  54. Liu H, Motoda H (1998) Feature selection for knowledge discovery and data mining. Kluwer Academic, Norwell

    MATH  Google Scholar 

  55. Wang C, Scott SD, Zhang J, Tao Q, Fomenko D, Gladyshev V (2004) A study in modeling low-conservation protein superfamilies. Technical report UNL-CSE-2004-0003, Department of Computer Science and Engineering, University of Nebraska, Lincoln, NE

  56. Kim J, Moriyama EN, Warr CG, Clyne PJ, Carlson JR (2000) Identification of novel multi-transmembrane proteins from genomic databases using quasi-periodic structural properties. Bioinform 16(9):767–775

    Article  Google Scholar 

  57. Littlestone N (1991) Redundant noisy attributes, attribute errors, and linear threshold learning using winnow. In: Proceedings of the 4th annual workshop on computational learning theory, Santa Cruz, CA, 1991, pp 147–156

  58. Bishop CM (1995) Neural networks for pattern recognition. Oxford University Press, New York

    Google Scholar 

  59. Dietterich TG (2000) Ensemble methods in machine learning. In: Kittler J, Roli F (eds) Lecture notes in computer science, vol 1867. Springer, Berlin, pp 1–15

    Google Scholar 

  60. Chang C-C, Lin C-J (2001) Libsvm: a library for support vector machines. Technical report, Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan

  61. Ray S, Craven M (2005) Supervised versus multiple instance learning: an empirical comparison. In: Proceedings of the 22nd international conference on machine learning, Bonn, Germany, 2005, pp 697–704

  62. Tan X, Chen S, Zhou Z-H, Liu J (2006) Learning non-metric partial similarity based on maximal margin criterion. In: Proceedings of the IEEE computer society conference on computer vision and pattern recognition, New York, NY, 2006, pp 138–145

Download references

Author information

Authors and Affiliations

  1. National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, 210093, China

    Min-Ling Zhang & Zhi-Hua Zhou

  2. College of Computer and Information Engineering, Hohai University, Nanjing, 210098, China

    Min-Ling Zhang

Authors
  1. Min-Ling Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhi-Hua Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zhi-Hua Zhou.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Zhang, ML., Zhou, ZH. Multi-instance clustering with applications to multi-instance prediction. Appl Intell 31, 47–68 (2009). https://doi.org/10.1007/s10489-007-0111-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10489-007-0111-x

Keywords

Search

Navigation