Knowledge Acquisition with Deep Fuzzy Inference Model and Its Application to a Medical Diagnosis

Abstract

In this paper, we reduce the number of fuzzy rules in the fuzzy inference model and acquire knowledge as fuzzy rules. The number of input items used for the inference model is reduced by randomly selecting the number of input items in each layer. Therefore, it turns out that the number of rules in the whole of this model can be reduced more than that of rules in an inference model that uses all the original input items at one time. However, in the previous model by Zhang, although the consequent part of the fuzzy rule was learned, the antecedent part was not learned. Since we need to deal with the situation where there is no prior knowledge in the problem to apply and it will be necessary to acquire knowledge from data, it is required to learn the antecedent part. In this paper, we propose a learning method for the antecedent fuzzy sets in fuzzy rules in order to obtain relationship between input and output of the learning data from the actual data. Then, as an example, the proposed method is applied to medical diagnosis of diabetes, the accuracy of the previous method is compared with that of the proposed method.