A Fuzzy Classifier System That Generates Linguistic Rules for Pattern Classification Problems
Selected papers from the EEE/Nagoya-University World Wisepersons Workshop on Fuzzy Logic, Neural Networks, and Evolutionary Computation
Encyclopedia of Optimization
Higher order models for fuzzy random variables
Fuzzy Sets and Systems
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Genetic Fuzzy Systems: Recent Developments and Future Directions; Guest editors: Jorge Casillas, Brian Carse
Genetic learning of fuzzy rules based on low quality data
Fuzzy Sets and Systems
Multi-objective optimization of problems with epistemic uncertainty
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
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To configure a proficient athletics team, coaches combine their expertise with the analysis of data collected during training sessions and competitions. This data and knowledge are vague, thus fuzzy logic is appropriate for designing a decision model. In this paper we will use a Genetic Fuzzy System for designing a model that can help the trainer to assess the performance of a given athlete in the future, given a combination of historical data and expert knowledge. This decision model has interest as a real-world application of GFSs, but it also involves novel kinds of data, whose study is a current trend in machine learning. Examples of such data include subjective perceptions of mistakes of the athletes, the reconciliation of different measurements taken by different observers, and interval-valued training data. We will use a possibilistic representation of these categories of information, in combination with an extension principle-based reasoning method, and finally show that the quality of a GFS which is based in these last principles improves the results of the original formulation of the same algorithm.