The interpretability of prediction models is very important for decision management. The vast majority of existing prediction analysis models based on rough set theory lack a certain interpretability. To break through the existing framework, this article deeply integrates rough fuzzy sets and logistic regression to construct an interpretable prediction model for multiattribute information systems. First, the Jensen_Shannon divergence with statistical interpretability is used to capture the attribute information with strong correlation under on a level, and then an fresh attribute-oriented δ\delta-rough fuzzy set model is presented. The pessimistic and optimistic decision concepts of the proposed rough fuzzy set model are used to interpretability enhancement of data information. On this basis, an interpretable predictive analysis model is constructed by combining logistic regression model. The construction process of the prediction model is supported by sufficient interpretable information. The predicted result is a result of development trend with a certain interpretability. Finally, to assess the effectiveness and viability of the proposed prediction model, we conduct comparative experiments with other three prediction models accompanied with four prediction performance indexes. Experimental results display that the proposed prediction model has well-pleasing prediction performance and antinoise ability.

中文翻译：

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具有不确定周期性扰动控制增益函数的非线性系统：具有不变性的自适应模糊控制


预测模型的可解释性对于决策管理非常重要。现有的绝大多数基于粗糙集理论的预测分析模型缺乏一定的可解释性。为了突破现有框架，本文将粗糙模糊集和逻辑回归深度结合，构建了多属性信息系统的可解释预测模型。首先，利用具有统计可解释性的Jensen_Shannon散度来捕获同一水平下具有强相关性的属性信息，然后提出一种新颖的面向属性的δ\delta-粗糙模糊集模型。所提出的粗糙模糊集模型的悲观和乐观决策概念用于增强数据信息的可解释性。在此基础上，结合逻辑回归模型构建了可解释的预测分析模型。预测模型的构建过程有足够的可解释信息支持。预测结果是发展趋势的结果，具有一定的可解释性。最后，为了评估所提出的预测模型的有效性和可行性，我们与其他三种预测模型以及四个预测性能指标进行了比较实验。实验结果表明，所提出的预测模型具有良好的预测性能和抗噪能力。