Research on a Kind of Multi-objective Evolutionary Fuzzy System with a Flowing Data Pool and a Rule Pool for Interpreting Neural Networks

Using fuzzy rules to explain the neural network conforms with people’s way of thinking. Multi-objective evolutionary fuzzy systems can obtain the fuzzy rules with high accuracy and strong interpretability, but the efficiency is low because they are often limited by the complexity of the problem. Therefore, a multi-objective evolutionary fuzzy system algorithm with a flowing data pool and a rule pool (FPs-MOEFS) is proposed in this paper. Based on the multi-objective evolutionary learning of fuzzy rules, an iteratively updated data pool is introduced, so that the next evolution iteration can focus on the current unpredictable data and improve the ability of the algorithm to find the global optimum; a fixed-size flowing rule pool is introduced to guarantee the diversity of candidate rules during the evolution while reducing the encoding length. To further improve the level of evolution, the co-evolution algorithm is integrated into the multi-objective framework, and a set of fuzzy rules with a strong fitting ability and high interpretability are obtained. The interpretations of neural networks by fuzzy rules are visualized on the artificial data. Comparative studies with other algorithms on UCI datasets indicate the effectiveness of the proposed algorithm. The proposed method can provide interpretations in the form of fuzzy rules for any kind of neural network with high interpretability and high accuracy. Therefore, it has good application prospects in the fields that need to understand the decision-making process and reasons of neural networks, especially in the high-risk decision-making fields.