Skip to main content

Eager to be Lazy: Towards a Complexity-guided Textual Case-Based Reasoning System

  • Conference paper
  • First Online:
  • 1004 Accesses

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9969))

Abstract

Finding an ideal representation for a case-base is important for a CBR system. This choice of an ideal representation is guided by the complexity of the cases. Based on the needs of each individual case, richer features are used for representation if required. While the framework is fairly general, this paper demonstrates its effectiveness on text classification due to the ease of evaluation. Each test case is treated differently by the classifier, in that if a shallow representation is deemed adequate for assigning a class label, the algorithm does away with a richer representation which is computationally expensive to generate. We also provided a time-budgeted evaluation of our framework which suggests that it holds promise in minimizing redundant or misleading comparisons and minimize time without compromising on effectiveness.

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

Buying options

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

Learn about institutional subscriptions

Notes

  1. 1.

    http://kdd.ics.uci.edu/databases/reuters21578/reuters21578.html.

  2. 2.

    www.imdb.com/interfaces.

  3. 3.

    www.jmdb.de.

  4. 4.

    ftp://ftp.fu-berlin.de/pub/misc/movies/database/.

References

  1. Gabrilovich, E., Markovitch, S.: Harnessing the expertise of 70,000 human editors: Knowledge-based feature generation for text categorization. J. Mach. Learn. Res. 8, 2297–2345 (2007)

    Google Scholar 

  2. Fellbaum, C.: WordNet: An Electronic Lexical Database. Bradford Books, Cambridge (1998)

    MATH  Google Scholar 

  3. Deerwester, S., Dumais, S.T., Furnas, G.W., Landauer, T.K., Harshman, R.: Indexing by latent semantic analysis. J. Am. Soc. Inf. Sci. 41(6), 391–407 (1990)

    Article  Google Scholar 

  4. Massie, S., Wiratunga, N., Craw, S., Donati, A., Vicari, E.: From anomaly reports to cases. In: Weber, R.O., Richter, M.M. (eds.) ICCBR 2007. LNCS (LNAI), vol. 4626, pp. 359–373. Springer, Heidelberg (2007). doi:10.1007/978-3-540-74141-1_25

    Chapter  Google Scholar 

  5. Riesbeck, C.K., Schank, R.C.: Inside Case-Based Reasoning. Lawrence Erlbaum Associates Inc., Hillsdale (1989)

    Google Scholar 

  6. Lamontagne, L.: Textual cbr authoring using case cohesion. In: Proceedings of 3rd Textual Case-Based Reasoning Workshop at the 8th European Conference on CBR (2006)

    Google Scholar 

  7. Lang, K.: Newsweeder: Learning to filter netnews. In: Proceedings of the Twelfth International Conference on Machine Learning, pp. 331–339 (1995)

    Google Scholar 

  8. Koller, D., Sahami, M.: Hierarchically classifying documents using very few words. In: Proceedings of the Fourteenth International Conference on Machine Learning, ICML 1997, pp. 170–178. Morgan Kaufmann Publishers Inc., San Francisco (1997)

    Google Scholar 

  9. Gupta, R., Ratinov, L.: Text categorization with knowledge transfer from heterogeneous data sources. In: Proceedings of the 23rd National Conference on Artificial Intelligence AAAI 2008, vol. 2, pp. 842–847. AAAI Press (2008)

    Google Scholar 

  10. Liu, Y., Song, R., Chen, Y., Nie, J.-Y., Wen, J.-R.: Adaptive query suggestion for difficult queries. In: Proceedings of the 35th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2012, pp. 15–24. ACM, New York (2012)

    Google Scholar 

  11. Hassan, A., White, R.W., Wang, Y.-M.: Toward self-correcting search engines: using underperforming queries to improve search. In: Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2013, pp. 263–272. ACM, New York (2013)

    Google Scholar 

  12. Grubb, A., Bagnell, D.: Speedboost: Anytime prediction with uniform near-optimality. In: Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, AISTATS 2012, La Palma, Canary Islands, 21–23 April 2012, pp. 458–466 (2012)

    Google Scholar 

  13. Freitag, D.: Multistrategy learning for information extraction. In: Proceedings of the Fifteenth International Conference on Machine Learning, pp. 161–169. Morgan Kaufmann (1998)

    Google Scholar 

  14. Xu, C., Tao, D., Xu, C.: A survey on multi-view learning. CoRR, abs/1304.5634 (2013)

    Google Scholar 

  15. Cummins, L., Bridge, D.: Choosing a case base maintenance algorithm using a meta-case base. In: Bramer, M., Petridis, M., Nolle, L. (eds.) Research and Development in Intelligent Systems XXVIII, pp. 167–180. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to K. V. S. Dileep .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing AG

About this paper

Cite this paper

Dileep, K.V.S., Chakraborti, S. (2016). Eager to be Lazy: Towards a Complexity-guided Textual Case-Based Reasoning System . In: Goel, A., Díaz-Agudo, M., Roth-Berghofer, T. (eds) Case-Based Reasoning Research and Development. ICCBR 2016. Lecture Notes in Computer Science(), vol 9969. Springer, Cham. https://doi.org/10.1007/978-3-319-47096-2_6

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-47096-2_6

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-47095-5

  • Online ISBN: 978-3-319-47096-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics