Attribute subset selection is an important issue in data mining and information processing. However, most automatic methodologies consider only the relevance factor between samples while ignoring the diversity factor. This may not allow the utilization value of hidden information to be exploited. For this reason, we propose a hybrid model named intuitionistic fuzzy (IF) rough set to overcome this limitation. The model combines the technical advantages of rough set and IF set and can effectively consider the above-mentioned statistical factors. First, fuzzy information granules based on IF relations are defined and used to characterize the hierarchical structures of the lower and upper approximations of IF rough set within the framework of granular computing. Then, the computation of IF rough approximations and knowledge reduction in IF information systems are investigated. Third, based on the approximations of IF rough set, significance measures are developed to evaluate the approximation quality and classification ability of IF relations. Furthermore, a forward heuristic algorithm for finding one optimal reduct of IF information systems is developed using these measures. Finally, numerical experiments are conducted on public datasets to examine the effectiveness and efficiency of the proposed algorithm in terms of the number of selected attributes, computational time, and classification accuracy.

中文翻译：

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基于直观模糊粗糙集的粒结构和属性子集选择


属性子集选择是数据挖掘和信息处理中的一个重要问题。然而，大多数自动方法只考虑样本之间的相关性因素，而忽略多样性因素。这可能不允许隐藏信息的利用价值被利用。为此，我们提出了一种名为直觉模糊（IF）粗糙集的混合模型来克服这一限制。该模型结合了粗糙集和IF集的技术优点，能够有效考虑上述统计因素。首先，定义了基于IF关系的模糊信息粒，并用其在粒计算的框架内刻画了IF粗糙集的下近似和上近似的层次结构。然后，研究了中频粗近似的计算和中频信息系统中的知识约简。第三，基于IF粗糙集的近似，开发显着性度量来评估IF关系的近似质量和分类能力。此外，还使用这些措施开发了一种前向启发式算法，用于寻找 IF 信息系统的最佳缩减。最后，在公共数据集上进行数值实验，以检验所提出算法在所选属性数量、计算时间和分类精度方面的有效性和效率。