Skip to main content
SpringerLink
Log in

Exploration vs. Exploitation in Case-Base Maintenance: Leveraging Competence-Based Deletion with Ghost Cases

  • Conference paper
  • First Online:
Case-Based Reasoning Research and Development (ICCBR 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11156))

Included in the following conference series:

Abstract

Case-base maintenance research has extensively studied strategies for competence-retaining case base compression. Such approaches generally rely on the representativeness assumption that current case base contents can be used as a proxy for future problems when determining cases to retain. For mature case bases in stable domains, this assumption works well. However, representativeness may not hold for sparse case bases during initial case base growth, for dynamically changing domains, or when a case base built for one task is applied to cross-domain problem-solving in another. This paper presents a new method for competence-preserving deletion, Expansion-Contraction Compression (ECC), aimed at improving competence preservation when the representativeness assumption is only partially satisfied. ECC precedes compression with adaptation-based exploration of previously unseen parts of the problem space to create “ghost cases” and exploits them to broaden the range of cases available for competence-based deletion. Experimental results support that this method increases competence and quality retention for less representative case bases. They also reveal the unexpected result that ECC can improve retention of competence and quality even for representative case bases.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Angiulli, F.: Fast condensed nearest neighbor rule. In: Proceedings of the Twenty-second International Conference on Machine Learning, pp. 25–32. ACM, New York (2005)

    Google Scholar 

  2. Brighton, H., Mellish, C.: Identifying competence-critical instances for instance-based learners. In: Liu, H., Motoda, H. (eds.) Instance Selection and Construction for Data Mining, vol. 608, pp. 77–94. Springer, Berlin (2001). https://doi.org/10.1007/978-1-4757-3359-4_5

    Chapter  Google Scholar 

  3. Houses Data Set, May 2009. https://wiki.csc.calpoly.edu/datasets/wiki/Houses

  4. Cheah, Y.-W., Plale, B., Kendall-Morwick, J., Leake, D., Ramakrishnan, L.: A noisy 10GB provenance database. In: Daniel, F., Barkaoui, K., Dustdar, S. (eds.) BPM 2011. LNBIP, vol. 100, pp. 370–381. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-28115-0_35

    Chapter  Google Scholar 

  5. Cunningham, P., Nowlan, N., Delany, S., Haahr, M.: A case-based approach to spam filtering that can track concept drift. Technical report. TCD-CS-2003-16, Computer Science Department, Trinity College Dublin (2003)

    Google Scholar 

  6. D’Aquin, M., Lieber, J., Napoli, A.: Adaptation knowledge acquisition: a case study for case-based decision support in oncology. Comput. Intell. 22(3/4), 161–176 (2006)

    Article  MathSciNet  Google Scholar 

  7. Delany, S.J., Cunningham, P.: An analysis of case-base editing in a spam filtering system. In: Funk, P., González Calero, P.A. (eds.) ECCBR 2004. LNCS, vol. 3155, pp. 128–141. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-28631-8_11

    Chapter  Google Scholar 

  8. Dheeru, D., Karra Taniskidou, E.: UCI Machine Learning Repository (2017). http://archive.ics.uci.edu/ml

  9. Francis, A.G., Ram, A.: A comparative utility analysis of case-based reasoning and control-rule learning systems. In: Lavrac, N., Wrobel, S. (eds.) ECML 1995. LNCS, vol. 912, pp. 138–150. Springer, Heidelberg (1995). https://doi.org/10.1007/3-540-59286-5_54

    Chapter  Google Scholar 

  10. Hanney, K., Keane, M.T.: Learning adaptation rules from a case-base. In: Smith, I., Faltings, B. (eds.) EWCBR 1996. LNCS, vol. 1168, pp. 179–192. Springer, Heidelberg (1996). https://doi.org/10.1007/BFb0020610

    Chapter  Google Scholar 

  11. Hart, P.E.: The condensed nearest neighbor rule. IEEE Trans. Inf. Theory 14, 515–516 (1968)

    Article  Google Scholar 

  12. Hills, T., Todd, P., Lazer, D., Redish, A., Couzin, I.: Exploration versus exploitation in space, mind, and society. Trends Cogn. Sci. 19(1), 46–54 (2015)

    Article  Google Scholar 

  13. Hoover, W.: Transforming health care through big data. Technical report, Institute for Health Technology Transformation (2013)

    Google Scholar 

  14. Houeland, T.G., Aamodt, A.: The Utility problem for lazy learners - towards a non-eager approach. In: Bichindaritz, I., Montani, S. (eds.) ICCBR 2010. LNCS, vol. 6176, pp. 141–155. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-14274-1_12

    Chapter  Google Scholar 

  15. Jalali, V., Leake, D.: Harnessing hundreds of millions of cases: case-based prediction at industrial scale. In: Ram, A., Wiratunga, N. (eds.) Case-Based Reasoning Research and Development, ICCBR 2018, vol. 6880. Springer, Berlin (2018). https://doi.org/10.1007/978-3-642-23291-6

    Google Scholar 

  16. Jalali, V., Leake, D., Forouzandehmehr, N.: Ensemble of adaptations for classification: learning adaptation rules for categorical features. In: Goel, A., Díaz-Agudo, M.B., Roth-Berghofer, T. (eds.) ICCBR 2016. LNCS, vol. 9969, pp. 186–202. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-47096-2_13

    Chapter  Google Scholar 

  17. Jalali, V., Leake, D.: Extending case adaptation with automatically-generated ensembles of adaptation rules. In: Delany, S.J., Ontañón, S. (eds.) ICCBR 2013. LNCS, vol. 7969, pp. 188–202. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-39056-2_14

    Chapter  Google Scholar 

  18. Leake, D., Kinley, A., Wilson, D.: Linking adaptation and similarity learning. In: Proceedings of the Eighteenth Annual Conference of the Cognitive Science Society, pp. 591–596. Lawrence Erlbaum, Mahwah (1996)

    Google Scholar 

  19. Leake, D., Schack, B.: Flexible feature deletion: compacting case bases by selectively compressing case contents. In: Hüllermeier, E., Minor, M. (eds.) ICCBR 2015. LNCS, vol. 9343, pp. 212–227. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24586-7_15

    Chapter  Google Scholar 

  20. Leake, D., Schack, B.: Adaptation-guided feature deletion: testing recoverability to guide case compression. In: Goel, A., Díaz-Agudo, M.B., Roth-Berghofer, T. (eds.) ICCBR 2016. LNCS, vol. 9969, pp. 234–248. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-47096-2_16

    Chapter  Google Scholar 

  21. Leake, D., Sooriamurthi, R.: Managing multiple case-bases: dimensions and issues. In: Proceedings of the Fifteenth International Florida Artificial Intelligence Research Society Conference, pp. 106–110. AAAI Press, Menlo Park (2002)

    Google Scholar 

  22. Leake, D., Whitehead, M.: Case provenance: the value of remembering case sources. In: Weber, R.O., Richter, M.M. (eds.) ICCBR 2007. LNCS, vol. 4626, pp. 194–208. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-74141-1_14

    Chapter  Google Scholar 

  23. Leake, D.B., Wilson, D.C.: When experience is wrong: examining CBR for changing tasks and environments. In: Althoff, K.-D., Bergmann, R., Branting, L.K. (eds.) ICCBR 1999. LNCS, vol. 1650, pp. 218–232. Springer, Heidelberg (1999). https://doi.org/10.1007/3-540-48508-2_16

    Chapter  Google Scholar 

  24. Leake, D.B., Wilson, D.C.: Remembering why to remember: performance-guided case-base maintenance. In: Blanzieri, E., Portinale, L. (eds.) EWCBR 2000. LNCS, vol. 1898, pp. 161–172. Springer, Heidelberg (2000). https://doi.org/10.1007/3-540-44527-7_15

    Chapter  Google Scholar 

  25. Leake, D., Wilson, M.: How many cases do you need? Assessing and predicting case-base coverage. In: Ram, A., Wiratunga, N. (eds.) ICCBR 2011. LNCS, vol. 6880, pp. 92–106. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-23291-6_9

    Chapter  Google Scholar 

  26. Lu, N., Zhang, G., Lu, J.: Concept drift detection via competence models. Artif. Intell. 209, 11–28 (2014)

    Article  MathSciNet  Google Scholar 

  27. Mathew, D., Chakraborti, S.: Competence guided model for casebase maintenance. In: Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, pp. 4904–4908. International Joint Conferences on Artificial Intelligence (2017)

    Google Scholar 

  28. McDonnell, N., Cunningham, P.: A Knowledge-light approach to regression using case-based reasoning. In: Roth-Berghofer, T.R., Göker, M.H., Güvenir, H.A. (eds.) ECCBR 2006. LNCS, vol. 4106, pp. 91–105. Springer, Heidelberg (2006). https://doi.org/10.1007/11805816_9

    Chapter  Google Scholar 

  29. McSherry, D.: An adaptation heuristic for case-based estimation. In: Smyth, B., Cunningham, P. (eds.) EWCBR 1998. LNCS, vol. 1488, pp. 184–195. Springer, Heidelberg (1998). https://doi.org/10.1007/BFb0056332

    Chapter  Google Scholar 

  30. Plaza, E., McGinty, L.: Distributed case-based reasoning. Knowl. Eng. Rev. 20(3), 315–320 (2005)

    Article  Google Scholar 

  31. Richter, M.: Knowledge Containers (2005). http://pages.cpsc.ucalgary.ca/mrichter/Papers/Knowledge%20Containers.pdf

  32. Smyth, B., Cunningham, P.: The utility problem analysed. In: Smith, I., Faltings, B. (eds.) EWCBR 1996. LNCS, vol. 1168, pp. 392–399. Springer, Heidelberg (1996). https://doi.org/10.1007/BFb0020625

    Chapter  Google Scholar 

  33. Smyth, B., Keane, M.: Remembering to forget: a competence-preserving case deletion policy for case-based reasoning systems. In: Proceedings of the Thirteenth International Joint Conference on Artificial Intelligence, pp. 377–382. Morgan Kaufmann, San Mateo (1995)

    Google Scholar 

  34. Smyt, B., McKenna, E.: Footprint-based retrieval. In: Althoff, K.-D., Bergmann, R., Branting, L.K. (eds.) ICCBR 1999. LNCS, vol. 1650, pp. 343–357. Springer, Heidelberg (1999). https://doi.org/10.1007/3-540-48508-2_25

    Chapter  Google Scholar 

  35. van Someren, M., Surma, J., Torasso, P.: A utility-based approach to learning in a mixed case-based and model-based reasoning architecture. In: Proceedings of the Second International Conference on Case-Based Reasoning, pp. 477–488. Springer, Berlin (1997)

    Google Scholar 

  36. Wilke, W., Vollrath, I., Bergmann, R.: Using knowledge containers to model a framework for learning adaptation knowledge. In: ECML Workshop Notes. LIS, Faculty of Informatics and Statistics (1998)

    Google Scholar 

  37. Wilson, D., Leake, D.: Maintaining case-based reasoners: dimensions and directions. Comput. Intell. 17(2), 196–213 (2001)

    Article  Google Scholar 

  38. Wilson, D., Martinez, T.: Reduction techniques for instance-based learning algorithms. Mach. Learn. 38(3), 257–286 (2000)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, 47408, USA

    David Leake & Brian Schack

Authors
  1. David Leake
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Brian Schack
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to David Leake .

Editor information

Editors and Affiliations

  1. Wright State University, Dayton, OH, USA

    Michael T. Cox

  2. Mälardalen University, Västeras, Sweden

    Peter Funk

  3. Mälardalen University, Västeras, Sweden

    Shahina Begum

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Leake, D., Schack, B. (2018). Exploration vs. Exploitation in Case-Base Maintenance: Leveraging Competence-Based Deletion with Ghost Cases. In: Cox, M., Funk, P., Begum, S. (eds) Case-Based Reasoning Research and Development. ICCBR 2018. Lecture Notes in Computer Science(), vol 11156. Springer, Cham. https://doi.org/10.1007/978-3-030-01081-2_14

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-01081-2_14

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-01080-5

  • Online ISBN: 978-3-030-01081-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation