Data classification algorithm based on Information Granules included by AFS

Abstract

Data classification problem in the field of machine learning and pattern recognition is an important research content, but the classic data classification algorithms mainly use numeric as the basis of classification modeling or put the high accuracy as the only index of classification model, and these classification algorithms usually are unable to construct classification model for easy understanding. As a basic abstract structure supporting human-centered granular computing methods, information granules can construct easy-to-understand geometric structures for different types of data. In order to solve the above problems, this paper proposes a classification algorithm based on AFS(axiomatic fuzzy set) and information granules to enhance the interpretability of models so that people can better understand the relationship between different types of data. The model can be divided into four stages. In the first stage, each class of data is divided into different data subsets. In the second stage, the AFS membership degrees of the corresponding prototypes are calculated based on axiomatic fuzzy set theory and a series of data blocks are generated on each class based on these degrees. The third stage mainly generates supersphere information granules according to the reasonable granularity principle theory and confidence levels. In the final stage, the activation level is calculated by the distance between samples and the hypersphere information granules and confidence levels to determine the "If-Then" rules.