Feature selection is an essential preprocessing requirement when solving a classification problem. In this respect, the Relief algorithm and its derivatives have been demonstrated to be a class of successful feature selectors. However, the computational cost of these algorithms is very high when large-scale datasets are processed. To solve this problem, we propose the fast ReliefF algorithm based on the information granulation of instances (IG-FReliefF). The algorithm uses K-means to granulate the dataset and selects the significant granules among them using the criteria defined by information entropy and information granulation, and then evaluates each feature on the dataset composed of the selected granules. Extensive experiments show that the proposed algorithm is more efficient than the existing representative algorithms, especially on large-scale data sets, and the proposed algorithm is almost the same as the comparison algorithm in terms of classification performance.

在解决分类问题时，特征选择是必不可少的预处理要求。在这方面，救济算法及其派生方法已被证明是一类成功的特征选择器。但是，在处理大规模数据集时，这些算法的计算成本非常高。为了解决这个问题，我们提出了基于实例信息细化的快速ReliefF算法（IG-FReliefF）。该算法使用K均值对数据集进行粒化，并使用信息熵和信息粒化定义的标准在其中选择重要的粒，然后对由所选粒组成的数据集上的每个特征进行评估。大量实验表明，所提出的算法比现有的代表性算法更有效，