Feature selection and entropy theory are two efficacious data analysis tools for investigating uncertainty information processing in artificial intelligence. The fruitful marriage of the two has been an active research topic in knowledge discovery. Currently, most feature selection methods via entropy theory mainly focus on the information measures at a single granular level. However, it ignores the interaction between granular levels, which leads to the poor stability and accuracy of related methods. Hence, this article proposes a novel zentropy-based uncertainty measure to design a feature selection method by exploiting the granular level structure in knowledge space. Subsequently, by analyzing the granular level structure in decision data, the zentropy-based uncertainty measure and its properties are designed and analyzed to depict the uncertainty knowledge from whole and internal. Moreover, two importance measures are defined to evaluate features based on the designed uncertainty measure, and then a corresponding feature selection algorithm is developed. Finally, some experiments are carried out on public datasets to demonstrate that the proposed method can achieve state-of-the-art performance among methods, especially regarding stability and classification accuracy.

中文翻译：

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Human__obot 交互中情感意图理解的三层加权模糊支持向量回归


特征选择和熵理论是研究人工智能中不确定性信息处理的两种有效的数据分析工具。两人富有成效的婚姻一直是知识发现领域的一个活跃的研究课题。目前，大多数基于熵理论的特征选择方法主要关注单粒度级别的信息度量。然而，它忽略了粒度级别之间的相互作用，导致相关方法的稳定性和准确性较差。因此，本文提出了一种新颖的基于熵的不确定性度量，通过利用知识空间中的粒度级别结构来设计特征选择方法。随后，通过分析决策数据的粒度层次结构，设计并分析了基于熵的不确定性测度及其性质，从整体和内部刻画不确定性知识。此外，定义了两个重要性度量来基于设计的不确定性度量来评估特征，然后开发了相应的特征选择算法。最后，在公共数据集上进行了一些实验，以证明所提出的方法可以在方法中实现最先进的性能，特别是在稳定性和分类精度方面。