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Model Development and Incremental Learning Based on Case-Based Reasoning for Signal and Image Analysis

  • Petra PernerEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10149)

Abstract

Over the years, image mining and knowledge discovery gained importance to solving problems. They are used in developing systems for automatic signal analysis and interpretation. The issues of model building and adaption, allowing an automatic system to adjust to the changing environments and moving objects, became increasingly important. One method of achieving adaptation in model building and model learning is Case-Based Reasoning (CBR). Case-Based Reasoning can be seen as a reasoning method as well as an incremental learning and knowledge acquisition method. In this paper we provide an overview of the CBR process and its main features: similarity, memory organization, CBR learning, and case-base maintenance. Then we review, based on applications, what has been achieved so far. The applications we are focusing on are meta-learning for parameter selection, image interpretation, incremental prototype-based classification, novelty detection and handling, and 1-D signal interpretation represented by a 0_1 sequence. Finally, we will summarize the overall concept of CBR usage for model development and learning.

Keywords

Model development Incremental learning Case-Based Reasoning Similarity Signal and image interpretation Image segmentation Novelty detection 1/0 sequence interpretation Computational intelligence 

References

  1. 1.
    Aha, D.W., Kibler, D., Albert, M.K.: Instance-based learning algorithm. Mach. Learn. 6(1), 37–66 (1991)Google Scholar
  2. 2.
    Bagherjeiran, A., Eick, C.F.: Distance function learning for supervised similarity assessment. In: Perner, P. (ed.) Case-Based Reasoning on Images and Signals. Studies in Computational Intelligence, pp. 91–126. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  3. 3.
    Bergmann, R., Wilke, W.: On the role of abstraction in case-based reasoning. In: Smith, I., Faltings, B. (eds.) EWCBR 1996. LNCS (LNAI), vol. 1168, pp. 28–43. Springer, Heidelberg (1996). doi: 10.1007/BFb0020600 CrossRefGoogle Scholar
  4. 4.
    Bergmann, R., Richter, M., Schmitt, S., Stahl, A., Vollrath, I.: Utility-oriented matching: a new research direction for case-based reasoning. In: Schnurr, H.-P., et al. (eds.) Professionelles Wissensmanagement, pp. 20–30. Shaker Verlag (2001)Google Scholar
  5. 5.
    Bellazzi, R., Montani, S., Portinale, L.: Retrieval in a prototype-based case library: a case study in diabetes therapy revision. In: Smyth, B., Cunningham, P. (eds.) EWCBR 1998. LNCS (LNAI), vol. 1488, pp. 64–75. Springer, Heidelberg (1998). doi: 10.1007/BFb0056322 CrossRefGoogle Scholar
  6. 6.
    Bentley, J.: Multidimensional binary search trees used for associative searching. Commun. ACM 18(9), 509–517 (1975)CrossRefzbMATHGoogle Scholar
  7. 7.
    Bhanu, B., Dong, A.: Concepts learning with fuzzy clustering and relevance feedback. In: Perner, P. (ed.) MLDM 2001. LNCS (LNAI), vol. 2123, pp. 102–116. Springer, Heidelberg (2001). doi: 10.1007/3-540-44596-X_9 CrossRefGoogle Scholar
  8. 8.
    Bichindaritz, I.: Memory structures and organization in case-based reasoning. In: Perner, P. (ed.) Case-Based Reasoning on Images and Signals. Studies in Computational Intelligence, pp. 175–194. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  9. 9.
    Bichindaritz, I.: Mémoire: a framework for semantic interoperability of case-based reasoning systems in biology and medicine. Artif. Intell. Med. 36(2), 177–192 (2006)CrossRefGoogle Scholar
  10. 10.
    Branting, L.K.: Integrating generalizations with exemplar-based reasoning. In: Proceedings of the 11th Annual Conference of Cognitive Science Society. Ann Arbor, MI, Lawrence Erlbaum, pp. 129–146 (1989)Google Scholar
  11. 11.
    CBR Commentaries. Knowl. Eng. Rev. 20(3)Google Scholar
  12. 12.
    Craw, S.: Introspective learning to build Case-Based Reasoning (CBR) knowledge containers. In: Perner, P., Rosenfeld, A. (eds.) MLDM 2003. LNCS, vol. 2734, pp. 1–6. Springer, Heidelberg (2003). doi: 10.1007/3-540-45065-3_1 CrossRefGoogle Scholar
  13. 13.
    Fisher, D.H.: Knowledge acquisition via incremental conceptual clustering. Mach. Learn. 2(2), 139–172 (1987). Kluwer Academic Publishers, Hingham, MA, USAGoogle Scholar
  14. 14.
    Frucci, M., Perner, P., di Baja, G.S.: Case-based reasoning for image segmentation by watershed transformation. In: Perner, P. (ed.) Case-Based Reasoning on Signals and Images, pp. 319–353. Springer, Heidelberg (2007)Google Scholar
  15. 15.
    Holt, A., Bichindaritz, I., Schmidt, R., Perner, P.: Medical applications in case-based reasoning. Knowl. Eng. Rev. 20(3), 289–292 (2005)CrossRefGoogle Scholar
  16. 16.
    Iglezakis, I., Reinartz, T., Roth-Berghofer, T.R.: Maintenance memories: beyond concepts and techniques for case base maintenance. In: Funk, P., González Calero, Pedro, A. (eds.) ECCBR 2004. LNCS (LNAI), vol. 3155, pp. 227–241. Springer, Heidelberg (2004). doi: 10.1007/978-3-540-28631-8_18 CrossRefGoogle Scholar
  17. 17.
    Jaenichen, S., Perner, P.: Conceptual clustering and case generalization of two dimensional forms. Comput. Intell. 22(3/4), 177–193 (2006)MathSciNetCrossRefGoogle Scholar
  18. 18.
    Jain, A.K., Dubes, R.C.: Algorithms for Clustering Data. Prentice Hall Inc, Upper Saddle River (1988)zbMATHGoogle Scholar
  19. 19.
    Law, Y.-N., Zaniolo, C.: An adaptive nearest neighbor classification algorithm for data streams. In: Jorge, A.M., Torgo, L., Brazdil, P., Camacho, R., Gama, J. (eds.) PKDD 2005. LNCS (LNAI), vol. 3721, pp. 108–120. Springer, Heidelberg (2005). doi: 10.1007/11564126_15 CrossRefGoogle Scholar
  20. 20.
    Little, S., Salvetti, O., Perner, P.: Evaluation of feature subset selection, feature weighting, and prototype selection for biomedical applications. J. Softw. Eng. Appl. 3, 39–49 (2010)CrossRefGoogle Scholar
  21. 21.
    De Mantaras, R.L., Cunningham, P., Perner, P.: Emergent case-based reasoning applications. Knowl. Eng. Rev. 20(3), 325–328 (2005)CrossRefGoogle Scholar
  22. 22.
    Markou, M., Singh, S.: Novelty detection: a review – part 1. Stat. Approaches Sig. Process. 83(12), 2481–2497 (2003)CrossRefzbMATHGoogle Scholar
  23. 23.
    Nagy, G., Nartker, T.H.: Optical Character Recognition: An Illustrated Guide to the Frontier. Kluwer, London (1999)Google Scholar
  24. 24.
    Nilsson, M., Funk, P.: A case-based classification of respiratory sinus arrhythmia. In: Funk, P., González Calero, Pedro, A. (eds.) ECCBR 2004. LNCS (LNAI), vol. 3155, pp. 673–685. Springer, Heidelberg (2004). doi: 10.1007/978-3-540-28631-8_49 CrossRefGoogle Scholar
  25. 25.
    Pekalska, E., Duin, R.: The Dissimilarity Representation for Pattern Recognition. World Scientific, Singapore (2005)CrossRefzbMATHGoogle Scholar
  26. 26.
    Perner, P.: Introduction to case-based reasoning for signals and images. In: Perner, P. (ed.) Case-Based Reasoning on Signals and Images, pp. 1–4. Springer, Heidelberg (2007)Google Scholar
  27. 27.
    Perner, P.: Data Reduction Methods for Industrial Robots with Direct Teach-in-Programing, Second Unchanged Edition. IBAI Publishing, Fockendorf. ISBN 978-3-940501-16-5Google Scholar
  28. 28.
    Perner, P.: Why case-based reasoning is attractive for image interpretation. In: Aha, D.W., Watson, I. (eds.) ICCBR 2001. LNCS (LNAI), vol. 2080, pp. 27–43. Springer, Heidelberg (2001). doi: 10.1007/3-540-44593-5_3 CrossRefGoogle Scholar
  29. 29.
    Perner, P.: An architecture for a CBR image segmentation system. J. Eng. Appl. Artif. Intell. 12(6), 749–759 (1999)CrossRefGoogle Scholar
  30. 30.
    Perner, P., Perner, H., Müller, B.: Similarity guided learning of the case description and improvement of the system performance in an image classification system. In: Craw, S., Preece, A. (eds.) ECCBR 2002. LNCS (LNAI), vol. 2416, pp. 604–612. Springer, Heidelberg (2002). doi: 10.1007/3-540-46119-1_44 CrossRefGoogle Scholar
  31. 31.
    Perner, P.: Case-base maintenance by conceptual clustering of graphs. Eng. Appl. Artif. Intell. 19(4), 295–381 (2006)Google Scholar
  32. 32.
    Perner, P.: Concepts for novelty detection and handling based on a case-based reasoning process scheme. In: Perner, P. (ed.) ICDM 2007. LNCS (LNAI), vol. 4597, pp. 21–33. Springer, Heidelberg (2007). doi: 10.1007/978-3-540-73435-2_3 Google Scholar
  33. 33.
    Perner, P., Holt, A., Richter, M.: Image processing in case-based reasoning. Knowl. Eng. Rev. 20(3), 311–314 (2005)CrossRefGoogle Scholar
  34. 34.
    Perner, P.: Using CBR learning for the low-level and high-level unit of a image interpretation system. In: Singh, S. (ed.) Advances in Pattern Recognition, pp. 45–54. Springer, Heidelberg (1998)Google Scholar
  35. 35.
    Perner, P.: Prototype-based classification. Appl. Intell. 28(3), 238–246 (2008)CrossRefGoogle Scholar
  36. 36.
    Perner P.: A novel method for the interpretation of spectrometer signals based on delta-modulation and similarity determination. In: Barolli, L., Li, K.F., Enokido, T., Xhafa, F., Takizawa, M. (eds.) Proceedings IEEE 28th International Conference on Advanced Information Networking and Applications AINA 2014, Victoria, Canada, pp. 1154–1160 (2014). doi: 10.1109/AINA.2014.44
  37. 37.
    Perner, P.: Representation of 1-D signals by a 0_1 sequence and similarity-based interpretation: a case-based reasoning approach. In: Perner, P. (ed.) Machine Learning and Data Mining in Pattern Recognition. LNCS (LNAI), vol. 9729, pp. 728–739. Springer, Heidelberg (2016). doi: 10.1007/978-3-319-41920-6_55 CrossRefGoogle Scholar
  38. 38.
    Perner, P.: Case-based reasoning and the statistical challenges II. In: Gruca, A., Czachórski, T., Kozielski, S. (eds.). AISC, vol. 242, pp. 17–38. Springer, Heidelberg (2014). doi: 10.1007/978-3-319-02309-0_2 CrossRefGoogle Scholar
  39. 39.
    Perner, P., Attig, A.: Meta-learning for image processing based on case-based reasoning. In: Bichindaritz, I., Vaidya, S., Jain, A., Jain, L.C. (eds.) Computational Intelligence in Healthcare 4. SIC, vol. 309, pp. 229–264. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  40. 40.
    Perner, P.: Case-based reasoning for image analysis and interpretation. In: Chen, C., Wang, P.S.P. (eds.) Handbook on Pattern Recognition and Computer Vision, 3rd Edition, pp. 95–114. World Scientific Publisher (2005)Google Scholar
  41. 41.
    Perner, P.: Novelty detection and in-line learning of novel concepts according to a case-based reasoning process schema for high-content image analysis in system biology and medicine. Comput. Intell. 25(3), 250–263 (2009)MathSciNetCrossRefGoogle Scholar
  42. 42.
    Perner, P.: Concepts for novelty detection and handling based on a case-based reasoning process scheme. Eng. Appl. Artif. Intell. 22(1), 86–91 (2009)CrossRefGoogle Scholar
  43. 43.
    Richter, Michael, M.: Introduction. In: Lenz, Mario, Burkhard, Hans-Dieter, Bartsch-Spörl, Brigitte, Wess, Stefan (eds.). LNCS (LNAI), vol. 1400, pp. 1–15. Springer, Heidelberg (1998). doi: 10.1007/3-540-69351-3_1 CrossRefGoogle Scholar
  44. 44.
    Richter, M.M.: Similarity. In: Perner, P. (ed.) Case-Based Reasoning on Images and Signals. Studies in Computational Intelligence, pp. 1–21. Springer, Heidelberg (2008)Google Scholar
  45. 45.
    Sankoff, D., Kruskal, J.B. (eds.): Time Warps, String Edits, and Macromolecules: The Theory and Practice of Sequence Comparison. Addison-Wesley, Readings (1983)Google Scholar
  46. 46.
    Schank, R.C.: Dynamic Memory. A theory of reminding and learning in computers and people. Cambridge University Press, Cambridge (1982)Google Scholar
  47. 47.
    Schmidt, R., Gierl, L.: Temporal abstractions and case-based reasoning for medical course data: two prognostic applications. In: Perner, P. (ed.) MLDM 2001. LNCS (LNAI), vol. 2123, pp. 23–34. Springer, Heidelberg (2001). doi: 10.1007/3-540-44596-X_3 CrossRefGoogle Scholar
  48. 48.
    Shapiro, L.G., Atmosukarto, I., Cho, H., Lin, H.J., Ruiz-Correa, S.: Similarity-based retrieval for biomedical applications. In: Perner, P. (ed.) Case-Based Reasoning on Signals and Images. SIC, vol. 73, pp. 355–388. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  49. 49.
    Smith, E.E., Douglas, L.M.: Categories and Concepts. Harvard University Press, Cambridge (1981)CrossRefGoogle Scholar
  50. 50.
    Smith, L.B.: From global similarities to kinds of similarities: the construction of dimensions in development. In: Smith, L.B. (ed.) Similarity and analogical reasoning, pp. 146–178. Cambridge University Press, New York (1989)Google Scholar
  51. 51.
    Soares, C., Brazdil, P.B.: A meta-learning method to select the kernel width in support vector regression. Mach. Learn. 54, 195–209 (2004)CrossRefzbMATHGoogle Scholar
  52. 52.
    Stahl, A.: Learning feature weights from case order feedback. In: Aha, David, W., Watson, I. (eds.) ICCBR 2001. LNCS (LNAI), vol. 2080, pp. 502–516. Springer, Heidelberg (2001). doi: 10.1007/3-540-44593-5_35 CrossRefGoogle Scholar
  53. 53.
    Vuori, V., Laaksonen, I., Oja, E., Kangas, J.: Experiments with adaptation strategies for a prototype-based recognition system for isolated handwritten characters. Int. J. Doc. Anal. Recogn. 3(3), 150–159 (2001)CrossRefGoogle Scholar
  54. 54.
    Wallace, C.S.: Statistical and Inductive Inference by Minimum Message Length. Information Science and Statistics. Springer, Series (2005)zbMATHGoogle Scholar
  55. 55.
    Weihs, C., Ligges, U., Mörchen, F., Müllensiefen, M.: Classification in music research. J. Adv. Data Anal. Classif. 3(1), 255–291 (2007). SpringerMathSciNetCrossRefzbMATHGoogle Scholar
  56. 56.
    Wess, S., Globig, C.: Case-based and symbolic classification. In: Wess, S., Althoff, K.-D., Richter, M.M. (eds.) EWCBR 1993. LNCS, vol. 837, pp. 77–91. Springer, Heidelberg (1994). doi: 10.1007/3-540-58330-0_78 CrossRefGoogle Scholar
  57. 57.
    Wettschereck, D., Aha, D.W., Mohri, T.: A review and empirical evaluation of feature weighting methods for a class of lazy learning algorithms. Artif. Intell. Rev. 11, 273–314 (1997)CrossRefGoogle Scholar
  58. 58.
    Wilson, D.R., Martinez, T.R.: Improved heterogeneous distance functions. J. Artif. Intell. Res. 6, 1–34 (1997)MathSciNetzbMATHGoogle Scholar
  59. 59.
    Wunsch, G.: Systemtheorie der Informationstechnik. Akademische Verlagsgesellschaft, Leipzig (1971)Google Scholar
  60. 60.
    Xiong, N., Funk, P.: Building similarity metrics reflecting utility in case-based reasoning. J. Intell. Fuzzy Syst. 17(4), 407–416 (2006). IOS PresszbMATHGoogle Scholar
  61. 61.
    Xueyan, S., Petrovic, S., Sundar S.: A case-based reasoning approach to dose planning in radiotherapy. In: Wilson, D.C., Khemani, D. (eds.) The seventh international Proceedings of Conference on Case-Based Reasoning, Belfast, Northern Ireland, pp. 348–357 (2007)Google Scholar
  62. 62.
    Zhang, L., Coenen, F., Leng, P.: Formalising optimal feature weight settings in case-based diagnosis as linear programming problems. Knowl.-Based Syst. 15, 298–391 (2002)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Institute of Computer Vision and Applied Computer SciencesIBaILeipzigGermany

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