An Evolutionary Multiobjective Constrained Optimisation Approach for Case Selection: Evaluation in a Medical Problem
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.
KeywordsMultiobjective Optimisation Case Selection Evaluation Methodology Multiobjective Problem Multiobjective Evolutionary Algorithm
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- 2.Aha, D.W., Kiblerand, D., Albert, M.K.: Instance-based learning algorithms. Machine Learning 6, 37–66 (1991)Google Scholar
- 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
- 8.Hart, P.E.: The condensed nearest neighbor rule. IEEE Transaction on Information Theory 14, 515+ (1968)Google Scholar
- 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
- 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
- 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
- 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
- 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
- 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