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
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Competence-Guided Case-Base Editing Techniques
EWCBR '00 Proceedings of the 5th European Workshop on Advances in Case-Based Reasoning
EMO '01 Proceedings of the First International Conference on Evolutionary Multi-Criterion Optimization
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Expert Systems with Applications: An International Journal
Finding Prototypes For Nearest Neighbor Classifiers
IEEE Transactions on Computers
Mining competent case bases for case-based reasoning
Artificial Intelligence
Computing context-dependent temporal diagnosis in complex domains
Expert Systems with Applications: An International Journal
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
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
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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.