A dynamic rule-based classification model via granular computing
A dynamic rule-based classification model via granular computing
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DOI：
10.1016/j.ins.2021.10.065
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发表时间：
2021-11
期刊：
影响因子：
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通讯作者：
Jiaojiao Niu;De-gang Chen;Jinhai Li;Hui Wang
中科院分区：
文献类型：
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作者：
Jiaojiao Niu;De-gang Chen;Jinhai Li;Hui Wang
作为一种有效的数据表示和处理工具，颗粒计算已经被纳入到正式的决策环境中，用于寻找颗粒约简，以实现挖掘颗粒规则的任务。然而，颗粒规则的分类性能还没有得到评价，这种方法并不适合于动态数据。为了解决这一问题，本研究对颗粒约简进行更新，并对得到的颗粒规则进行分类性能评价。具体来说，我们首先对颗粒约简和颗粒规则的更新进行了理论分析，然后提出了一种基于更新机制的动态基于规则的分类模型（DRCM）。最后，我们讨论了该模型的可行性，并将其与几种流行的分类算法进行了比较。实验表明，颗粒约简可以在一定程度上提高分类能力，并且在一些一致性数据集上，DRCM可以获得更好的分类性能。
As an effective tool for data representation and processing, granular computing has been incorporated into formal decision contexts for finding granular reducts to achieve the task of mining granular rules. However, the classification performance of granular rules has not been evaluated, and this type of method is not suitable for dynamic data. To solve this problem, the current study updates granular reducts and evaluates the obtained granular rules in terms of classification performance. Concretely, we first give a theoretical analysis of updating granular reducts and granular rules and then present a novel dynamic rule-based classification model (DRCM) based on the updating mechanism. Finally, we discuss the feasibility of the proposed model and compare it with several popular classification algorithms. The conducted experiments demonstrate that the granular reducts can improve the classification ability to a certain extent and that DRCM can achieve better classification performance on some consistent datasets.