A visual approach for fuzzy rule induction

Models are descriptions of real facts that serve us to think and reason. In building models, a compromise always exists between accuracy, that is, how precisely the model describes reality, and simplicity, without which the model would be useless. So, a good model must be simple and intuitive while being accurate enough. In this paper we propose a novel approach based on visual techniques aiming to help the human in fine-tuning fuzzy decision trees to enhance its interpretability and insightfulness with a minimal loss of accuracy. By involving the human in the design process, these techniques allow to include prior knowledge in the selection of membership functions as well as to assess the significance of rules in the model to help in the pruning stage.