Rule Reduction for EBRB Classification Based on Clustering

Abstract

The extended belief rule base (EBRB) system has been successfully applied to classification problems in various fields. However, the existing EBRB generation method converts all data into extended belief rules, which leads to the large scale of rule base and affects the efficiency and accuracy of subsequent inference. In view of this, this paper proposes an EBRB rule reduction method based on the adaptive K-means clustering algorithm (RC-EBRB). In the rule generation process, the K-means clustering algorithm is applied to obtain the rule cluster centers, which are used to generate new rules. In the end, these new rules form a reduced EBRB. Moreover, in order to determine the initial cluster centers and the number of clusters in the K-means clustering algorithm, the algorithm idea of K-means++ is introduced and a reduction granularity adjustment algorithm with threshold is proposed, respectively. Finally, four datasets on commonly used classification datasets from UCI are used to verify the performance of the proposed method. The experimental results are compared with the existing EBRB methods and the traditional machine learning methods, which prove the effectiveness of the method.