Computational approaches to analogical reasoning: a comparative analysis
Artificial Intelligence
Instance-Based Learning Algorithms
Machine Learning
Tolerating noisy, irrelevant and novel attributes in instance-based learning algorithms
International Journal of Man-Machine Studies - Special issue: symbolic problem solving in noisy and novel task environments
Nearest neighbor classifier: simultaneous editing and feature selection
Pattern Recognition Letters - Special issue on pattern recognition in practice VI
Reduction Techniques for Instance-BasedLearning Algorithms
Machine Learning
Learning and reasoning by analogy
Communications of the ACM
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Modelling the Competence of Case-Bases
EWCBR '98 Proceedings of the 4th European Workshop on Advances in Case-Based Reasoning
Competence-Guided Case-Base Editing Techniques
EWCBR '00 Proceedings of the 5th European Workshop on Advances in Case-Based Reasoning
Building Compact Competent Case-Bases
ICCBR '99 Proceedings of the Third International Conference on Case-Based Reasoning and Development
Categorizing Case-Base Maintenance: Dimensions and Directions
EWCBR '98 Proceedings of the 4th European Workshop on Advances in Case-Based Reasoning
Expert Systems with Applications: An International Journal
Fault diagnosis in industry using sensor readings and case-based reasoning
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - AILS '04
Finding Prototypes For Nearest Neighbor Classifiers
IEEE Transactions on Computers
Mining competent case bases for case-based reasoning
Artificial Intelligence
Remembering to forget: a competence-preserving case deletion policy for case-based reasoning systems
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Case-based retrieval to support the treatment of end stage renal failure patients
Artificial Intelligence in Medicine
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
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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.