Imbalanced datasets in the generation of fuzzy classification systems - An investigation using a multiobjective evolutionary algorithm based on decomposition

In the last years, multi-objective evolutionary algorithms have been used to learn or tune components of fuzzy systems from data. The suitability of such algorithms for this task is due to the possibility of balancing the conflicting objectives of accuracy and interpretability of the resulting model. In a previous work, a method to learn fuzzy classification rules from imbalanced datasets using multi-objective genetic algorithms and the iterative rule learning approach was proposed by the authors. In this method, the imbalanced datasets are pre-processed to be transformed to balanced datasets, and then the rules are generated and the fuzzy sets are tuned. The method has been evaluated in an experimental study considering ten different methods to pre-process the imbalanced datasets and presented competitive results with comparison to similar proposals. The genetic generation of the rules and the optimization of fuzzy sets were both based on NSGA-II. The objective of this article is to investigate whether the multi-objective algorithm used can impact the performance of the method. In this direction, the work developed here presents and analyses the results obtained by the method proposed before using MOEA/D instead of NSGA-II. The analysis demonstrate that the accuracy obtained with MOEA/D is similar to that of NSGA-II while the interpretability measures such as number of rules and number of conditions tend to be better.