Fuzzy three-way rule learning and its classification methods

Abstract

Rules play a crucial role in classification tasks, driving the advancement of artificial intelligence. However, how to improve the interpretability of extracted rules while ensuring the performance of classification tasks is always a challenge, owing to the diversity of data types. Since three-way decision rules derive and explain from positive and negative aspects and provide more detailed information than general rules, this article explores fuzzy three-way rule learning from the perspective of two-way granular reduct by taking the FCA-based granular computing method as a framework. Specifically, we first present the object-induced fuzzy three-way granular rules and the object-induced two-way fuzzy three-way rules. Then, the fuzzy three-way rule-based dynamic updating method (FTRDUM) and the weight-based voting method are proposed to improve the classification performance. Finally, to illustrate the effectiveness of FTRDUM, some numerical experiments are conducted. The results show the superiority of the proposed algorithm in classification accuracy.