For the first time, the ensemble feature selection is modeled as a Multi-Criteria Decision-Making (MCDM) process in this paper. For this purpose, we used the VIKOR method as a famous MCDM algorithm to rank the features based on the evaluation of several feature selection methods as different decision-making criteria. Our proposed method, EFS-MCDM, first obtains a decision matrix using the ranks of every feature according to various rankers. The VIKOR approach is then used to assign a score to each feature based on the decision matrix. Finally, a rank vector for the features generates as an output in which the user can select a desired number of features. We have compared our approach with some ensemble feature selection methods using feature ranking strategy and basic feature selection algorithms to illustrate the proposed method's optimality and efficiency. The results show that our approach in terms of accuracy, F-score, and algorithm run-time is superior to other similar methods and performs in a short time, and it is more efficient than the other methods.

本文首次将集成特征选择建模为多标准决策（MCDM）过程。为此，我们使用 VIKOR 方法作为著名的 MCDM 算法，基于对几种特征选择方法的评估作为不同的决策标准对特征进行排序。我们提出的方法 EFS-MCDM 首先根据不同的等级使用每个特征的等级获得一个决策矩阵。然后使用 VIKOR 方法根据决策矩阵为每个特征分配一个分数。最后，生成特征的秩向量作为输出，用户可以在其中选择所需数量的特征。我们将我们的方法与一些使用特征排序策略和基本特征选择算法的集成特征选择方法进行了比较，以说明所提出的方法 s 最优性和效率。结果表明，我们的方法在准确率、F-score 和算法运行时间方面优于其他类似方法，并且在短时间内执行，并且比其他方法更有效。