Construction of fuzzy classification systems by fitness sharing based genetic search and boosting based ensemble

This paper concentrates on the development of precise fuzzy rule-based classification systems for high-dimensional and multi-class problems. The approach begins with the extraction on potential fuzzy if-then rules using fitness sharing based genetic algorithms, this ensures effective searching for productive niches, thereby evolving and maintaining a diverse, cooperative population. Subsequently, for the purpose of combining the obtained fuzzy rules and eliminating their conflicts, an adaboost ensemble method is utilized, enhancing the accuracy of the fuzzy classification systems.

Experiments have been conducted on 10 UCI datasets and 3 well-known image classification problems. The features for these image tasks were derived from the activation values of the final convolutional layer in pre-trained convolutional neural networks. These datasets, which were chosen to evaluate the effectiveness of the proposed approach, exhibit significant variation in terms of dimensionality and the number of class labels. Comparative analyses are carried out with conventional fuzzy rule-based classification methods, and the results demonstrate that the classification systems can be developed for complex problems, while maintaining a high-level of prediction accuracy.