Deep Modular Fuzzy Inference Model
Haruya Nagai, Hirosato Seki · 2023
In recent years, explainable AI (XAI) has been emphasized in various fields such as healthcare, finance, etc. Since the fuzzy rules in fuzzy inference models are structured as If-Then rules, the inference process of fuzzy inference models can be easily understood by humans. Among them, a deep TSK fuzzy inference model has fuzzy rules with low complexities due to select randomly features and map randomly them to fuzzy partitions. However, this randomness may lead to situations where important features or fuzzy partitions of them that have a significant impact on the results are not used in the inference. Therefore, this paper propose a deep modular fuzzy inference model with high accuracy and stability using small number of fuzzy rules with high interpretability.