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.
This study was partially financed by the Spanish MEC through projects TIN2009-14372-C03-01 and PET2007-0033 and the project 15277/PI/10 funded by Seneca Agency of Science and Technology of the Region of Murcia within the II PCTRM 2007-2010.
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References
Aha, D.W.: Tolerating noisy, irrelevant and novel attributes in instance-based learning algorithms. International Journal of Man-Machine Studies 36, 267–287 (1992)
Aha, D.W., Kiblerand, D., Albert, M.K.: Instance-based learning algorithms. Machine Learning 6, 37–66 (1991)
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)
Chang, C.L.: Finding prototypes for nearest neighbor classifiers. IEEE Transactions on Computers C 23, 1179–1184 (1974)
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)
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)
Deb, K., Kalyanmoy, D. (eds.): Multi-Objective Optimization Using Evolutionary Algorithms. John Wiley & Sons, Inc., New York (2001)
Hart, P.E.: The condensed nearest neighbor rule. IEEE Transaction on Information Theory 14, 515+ (1968)
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)
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)
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)
Kuncheva, L.I., Jain, L.C.: Nearest neighbor classifier: Simultaneous editing and feature selection. Pattern Recognition Letters 20, 1149–1156 (1999)
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)
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)
McSherry, D.: Automating case selection in the construction of a case library. Knowledge-Based Systems 13, 133–140 (2000)
Nersessian, N.: The Cognitive Basis of Model-based Reasoning in Science. In: The Cognitive Basis of Science. ch. 7. Cambridge University Press (2002)
Pan, R., Yang, Q., Pan, S.J.: Mining competent case bases for case-based reasoning. Artificial Intelligence 171, 1039–1068 (2007)
Parrillo, J.E.: Septic shock - vasopressin, norepinephrina, and urgency. The New England Journal of Medicine 358(9), 954–956 (2008)
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)
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)
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)
Tomek, I.: An experiment with the edited nearest-neighbor rule. IEEE Transactions on Systems Man and Cybernetics 6, 448–452 (1976)
Wilson, D.R., Martinez, T.R.: Reduction techniques for instance-based learning algorithms. Machine Learning 38, 257–286 (2000)
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)
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Lupiani, E., Jimenez, F., Juarez, J.M., Palma, J. (2011). An Evolutionary Multiobjective Constrained Optimisation Approach for Case Selection: Evaluation in a Medical Problem. In: Lozano, J.A., Gámez, J.A., Moreno, J.A. (eds) Advances in Artificial Intelligence. CAEPIA 2011. Lecture Notes in Computer Science(), vol 7023. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25274-7_39
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