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A multiobjective genetic fuzzy system for classification problems is presented. Its advantage is that it uses 3-parameter membership function tuning with dynamic constraints. Therefore, the accuracy is improved without deteriorating the transparency of fuzzy partitions. The initial population is created with a method, which reduces the search space by removing irrelevant input variables. Then, multiobjective...
In the last few years a number of studies have focused on the design of fuzzy rule-based systems which are interpretable (i.e. simple and easy to read), while maintaining quite a high level of accuracy. Therefore, a new tendency in the fuzzy modeling that looks for a good balance between interpretability and accuracy is increasing in importance. In fact, recently multi-objective evolutionary algorithms...
Recently, multiobjective evolutionary algorithms have been applied to improve the difficult tradeoff between interpretability and accuracy of fuzzy rule-based systems. It is known that both requirements are usually contradictory, however, these kinds of algorithms can obtain a set of solutions with different trade-offs. The application of multiobjective evolutionary algorithms to fuzzy rule-based...
When questionnaires are designed, each factor under study can be assigned a set of different items. The answers to these questions must be merged in order to obtain the level of that input. Therefore, it is typical for data acquired from questionnaires that each of the inputs and outputs are not numbers, but sets of values. In this paper, we represent the information contained in such a set of values...
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