Hierarchical sequential three-way decision model

Knowledge acquisition is one of the important issues in granular computing. In recent years, scholars have paid much attention to this problem and proposed the rule-based acquisition algorithms. However, a large number of the decision rules mined by the ex -isting algorithms are not comprehensible. At the same time, the long detailed rules are easy to lead to over-fitting. In order to generate simpler and easier-to-comprehensible rules and improve human decision-making, a hierarchical sequential three-way decision model is proposed by combining sequential three-way decisions with hierarchical rough set model. Specifically, we generalize the concepts of the conditional attributes through the concept hierarchy tree, design the multi-hierarchical decision table with multiple levels of granu-larity, and illustrate the corresponding algorithm to acquire the generalized rules step by step. The experimental results demonstrate that the proposed model can mine hierarchical sequential three-way decision rules under different levels of granularity. (C) 2021 Elsevier Inc. All rights reserved.

Automated summary

This paper proposes a hierarchical sequential three-way decision model that combines sequential three-way decisions with hierarchical rough set model, generalizes conditional attributes concepts through concept hierarchy tree, designs multi-level granularity multi-hierarchical decision tables, and illustrates the algorithm for acquiring generalized rules step by step. Experimental results demonstrate that the model can mine hierarchical sequential three-way decision rules under different levels of granularity.