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Hierarchical sequential three-way decision model
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DOI：10.1016/j.ijar.2021.10.004.png)
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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.
Keyword:
Decision rule mining
Sequential three-way decisions
Hierarchical rough set model
Concept hierarchy tree
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被引数:
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