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Optimal granularity selection based on cost-sensitive sequential three-way decisions with rough fuzzy sets

机译：基于成本敏感的具有粗糙模糊集的顺序三路决策的最佳粒度选择

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摘要

As an extension of Pawlak’s rough sets, rough fuzzy sets is proposed to deal with the target concept which is typically fuzzy or uncertain. It is worthwhile to introduce cost-sensitive learning into granular computing, as the granularity with the optimal cost can be selected. In the terms of decision making, test cost and decision cost are the most popular cost types. In the sequential three-way decisions (S3WD) models, the granularity is sensitive to the test cost. Meanwhile, the accuracy is sensitive to the decision cost. Selecting an optimal cost-sensitive granularity for problem solving is helpful for achieving optimal results at the lowest total cost in S3WD. However, it is difficult to evaluate test cost precisely and objectively in real-life applications, and existing works only focus on searching for the total cost as the objective function. In this paper, we firstly present a sequential three-way decisions model with rough fuzzy sets (S3WDRFS). Then, for S3WDRFS and its three regions, the changing rules of their decision cost are revealed in a hierarchical granular structure. By considering user requirements, we propose an optimization mechanism to achieve the optimal cost-sensitive granularity selection based on S3WDRFS model. Finally, the experimental results demonstrate that exemplary optimal granularities can be obtained for high quality decision-making under certain constraints.
机译：作为Pawlak粗糙集的扩展，提出了粗糙模糊集来处理通常是模糊或不确定的目标概念。将成本敏感型学习引入粒度计算是值得的，因为可以选择具有最佳成本的粒度。在决策方面，测试成本和决策成本是最受欢迎的成本类型。在顺序三向决策（S3WD）模型中，粒度对测试成本敏感。同时，准确性对决策成本敏感。为解决问题选择最佳的成本敏感粒度有助于在S3WD中以最低的总成本获得最佳结果。但是，在实际应用中很难准确，客观地评估测试成本，而现有的工作仅着眼于寻找总成本作为目标函数。在本文中，我们首先提出了具有粗糙模糊集的顺序三路决策模型（S3WDRFS）。然后，对于S3WDRFS及其三个区域，其决策成本的变化规则以分层粒度结构显示。通过考虑用户需求，我们提出了一种基于S3WDRFS模型的优化机制，以实现对成本敏感的最佳粒度选择。最后，实验结果表明，可以在某些约束条件下获得示例性的最佳粒度，以进行高质量的决策。

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