Evaluating Case Selection Algorithms for Analogical Reasoning Systems
An essential issue for developing analogical reasoning systems (such as Case-Based Reasoning systems) is to build the case memory by selecting registers from an external database. This issue is called case selection and the literature provides a wealth of algorithms to deal with it. For any particular domain, to choose the case selection algorithm is a critical decision on the system design. Despite some algorithms obtain good results, a specific algorithms evaluation is needed. Most of the efforts done in this line focus on the number of registers selected and providing a simple evaluation of the system obtained. In some domains, however, the system must fulfil certain constraints related to accuracy or efficiency. For instance, in the medical field, specificity and sensitivity are critical values for some tests. In order to partially solve this problem, we propose an evaluation methodology to obtain the best case selection method for a given memory case. In order to demonstrate the usefulness of this methodology, we present new case selection algorithms based on evolutionary multi-objective optimization. We compare the classical algorithms and the multi-objective approach in order to select the most suitable case selection algorithm according to different standard problems.
KeywordsCase Selection Evaluation Methodology Reasoning System Breast Cancer Dataset Neighbor Rule
Unable to display preview. Download preview PDF.
- 1.Aamodt, A., Plaza, E.: Case-based reasoning: Foundational issues, methodological variations, and system approaches. AI Communications 7, 39–59 (1994)Google Scholar
- 3.Aha, D.W., Kiblerand, D., Albert, M.K.: Instance-based learning algorithms. Machine Learning 6, 37–66 (1991)Google Scholar
- 10.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.Kibler, D., Aha, D.W.: Learning representative exemplars of concepts: An initial case study. In: Proceedings of the Fourth International Workshop on Machine Learning, pp. 24–30 (1987)Google Scholar
- 12.Kling, R.E.: A paradigm for reasoning by analogy. Artificial Intelligence (2),147–148 (1971)Google Scholar
- 13.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. AAAI, Menlo Park (1996)Google Scholar
- 20.Olsson, E., Funk, P., Xiong, N.: Fault diagnosis in industry using sensor readings and case-based reasoning. Journal of Intelligent and Fuzzy Systems 15(1), 41–46 (2004)Google Scholar
- 23.Smyth, B., Keane, M.T.: Remembering to forget - A competence-preserving case deletion policy for case-based reasoning systems. In: Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence, IJCAI 1995), August 20-25, vol. 1 and 2, pp. 377–382 (1995)Google Scholar
- 30.Wilson, D.R., Martinez, T.R.: Instance pruning techniques. In: Machine Learning: Proceedings of the Fourteenth International Conference (ICML 1997), pp. 404–411. Morgan Kaufmann, San Francisco (1997)Google Scholar
- 33.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. International Center for Numerical Methods in Engineering (2001)Google Scholar