An Evolutionary Multiobjective Constrained Optimisation Approach for Case Selection: Evaluation in a Medical Problem

  • Eduardo Lupiani
  • Fernando Jimenez
  • José M. Juarez
  • José Palma
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7023)

Abstract

A solid building process and a good evaluation of the knowledge base are essential in the clinical application of Case-Based Reasoning Systems. Unlike other approaches, each piece of the knowledge base (cases of the case memory) is knowledge-complete and independent from the rest. Therefore, the main issue to build a case memory is to select which cases must be included or removed. Literature provides a wealth of methods based on instance selection from a database. However, it can be also understood as a multiobjective problem, maximising the accuracy of the system and minimising the number of cases in the case memory. Most of the efforts done in this evaluation of case selection methods focus on the number of registers selected, providing an evaluation of the system based on its accuracy. On the one hand, some case selection methods follow a non deterministic approach. Therefore, a rough evaluation could entail to inaccurate conclusions. On the other hand, specificity and sensitivity are critical values to evaluate tests in the medical field. However, these parameters are hardly ever included in the case selection evaluation. In order to partially solve this problem, we propose an evaluation methodology to obtain the best case selection method for a given memory case. We also propose a case selection method based on multiobjective constrained optimisation for which Evolutionary Algorithms are used. Finally, we illustrate the use of this methodology by evaluating classic and the case selection method proposed, in a particular problem of Burn Intensive Care Units.

Keywords

Multiobjective Optimisation Case Selection Evaluation Methodology Multiobjective Problem Multiobjective Evolutionary Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Aha, D.W.: Tolerating noisy, irrelevant and novel attributes in instance-based learning algorithms. International Journal of Man-Machine Studies 36, 267–287 (1992)CrossRefGoogle Scholar
  2. 2.
    Aha, D.W., Kiblerand, D., Albert, M.K.: Instance-based learning algorithms. Machine Learning 6, 37–66 (1991)Google Scholar
  3. 3.
    Ahn, H., Kim, K., Han, I.: A case-based reasoning system with the two-dimensional reduction technique for customer classification. Expert Systems With Applications 32, 1011–1019 (2007)CrossRefGoogle Scholar
  4. 4.
    Chang, C.L.: Finding prototypes for nearest neighbor classifiers. IEEE Transactions on Computers C 23, 1179–1184 (1974)CrossRefMATHGoogle Scholar
  5. 5.
    Coello Coello, C.A., Lamont, G.L., van Veldhuizen, D.A.: Evolutionary Algorithms for Solving Multi-Objective Problems. In: Genetic and Evolutionary Computation, 2nd edn. Springer, Heidelberg (2007)Google Scholar
  6. 6.
    Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation 6, 182–197 (2002)CrossRefGoogle Scholar
  7. 7.
    Deb, K., Kalyanmoy, D. (eds.): Multi-Objective Optimization Using Evolutionary Algorithms. John Wiley & Sons, Inc., New York (2001)MATHGoogle Scholar
  8. 8.
    Hart, P.E.: The condensed nearest neighbor rule. IEEE Transaction on Information Theory 14, 515+ (1968)Google Scholar
  9. 9.
    Jara, A., Martinez, R., Vigueras, D., Sanchez, G., Jimenez, F.: Attribute selection by multiobjective evolutionary computation applied to mortality from infection severe burns patients. In: Proceedings of the International Conference of Health Informatics (HEALTHINF 2011), Algarbe, Portugal, pp. 467–471 (2011)Google Scholar
  10. 10.
    Juarez, J.M., Campos, M., Palma, J., Marin, R.: Computing context-dependent temporal diagnosis in complex domains. Int. J. Expert Sys. with App. 35(3), 991–1010 (2007)CrossRefGoogle Scholar
  11. 11.
    Kolodner, J.L.: Making the Implicit Explicit: Clarifying the Principles of Case-Based Reasoning. In: Case-based Reasoning: Experiences, Lessons and Future Directions. ch. 16, pp. 349–370. American Association for Artificial Intelligence (1996)Google Scholar
  12. 12.
    Kuncheva, L.I., Jain, L.C.: Nearest neighbor classifier: Simultaneous editing and feature selection. Pattern Recognition Letters 20, 1149–1156 (1999)CrossRefGoogle Scholar
  13. 13.
    Laumanns, M., Zitzler, E., Thiele, L.: On the Effects of Archiving, Elitism, and Density Based Selection in Evolutionary Multi-objective Optimization. In: Zitzler, E., Deb, K., Thiele, L., Coello Coello, C.A., Corne, D.W. (eds.) EMO 2001. LNCS, vol. 1993, pp. 181–196. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  14. 14.
    McKenna, E., Smyth, B.: Competence-guided Case-base Editing Techniques. In: Blanzieri, E., Portinale, L. (eds.) EWCBR 2000. LNCS (LNAI), vol. 1898, pp. 186–197. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  15. 15.
    McSherry, D.: Automating case selection in the construction of a case library. Knowledge-Based Systems 13, 133–140 (2000)CrossRefGoogle Scholar
  16. 16.
    Nersessian, N.: The Cognitive Basis of Model-based Reasoning in Science. In: The Cognitive Basis of Science. ch. 7. Cambridge University Press (2002)Google Scholar
  17. 17.
    Pan, R., Yang, Q., Pan, S.J.: Mining competent case bases for case-based reasoning. Artificial Intelligence 171, 1039–1068 (2007)MathSciNetCrossRefMATHGoogle Scholar
  18. 18.
    Parrillo, J.E.: Septic shock - vasopressin, norepinephrina, and urgency. The New England Journal of Medicine 358(9), 954–956 (2008)CrossRefGoogle Scholar
  19. 19.
    Ritter, G.L., Woodruff, H.B., Lowry, S.R., Isenhour, T.L.: Algorithm for a selective nearest neighbor decision rule. IEEE Transactions on Information Theory 21, 665–669 (1975)CrossRefMATHGoogle Scholar
  20. 20.
    Smyth, B., Keane, M.T.: Remembering to forget - A competence-preserving case deletion policy for case-based reasoning systems. In: 14th International Joint Conference on Artificial Intelligence (IJCAI 1995), Montreal, Canada (August 1995)Google Scholar
  21. 21.
    Thombs, B.D., Singh, V.A., Halonen, J., Diallo, A., Milner, S.M.: The effects of preexisting medical comorbidities on mortality and length of hospital stay in acute burn injury: evidence from a national sample of 31,338 adult patients. Ann. Surg. 245(4), 626–634 (2007)CrossRefGoogle Scholar
  22. 22.
    Tomek, I.: An experiment with the edited nearest-neighbor rule. IEEE Transactions on Systems Man and Cybernetics 6, 448–452 (1976)MathSciNetCrossRefMATHGoogle Scholar
  23. 23.
    Wilson, D.R., Martinez, T.R.: Reduction techniques for instance-based learning algorithms. Machine Learning 38, 257–286 (2000)CrossRefMATHGoogle Scholar
  24. 24.
    Zitzler, E., Laumanns, M., Thiele, L.: SPEA2: Improving the strength pareto evolutionary algorithm for multiobjective optimization. In: Evolutionary Methods for Design Optimization and Control with Applications to Industrial Problems, Athens, Greece, pp. 95–100 (2001)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Eduardo Lupiani
    • 1
  • Fernando Jimenez
    • 1
  • José M. Juarez
    • 1
  • José Palma
    • 1
  1. 1.Computer Science FacultyUniversidad de MurciaSpain

Personalised recommendations