Research Area:  Machine Learning
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 methods 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.
Author(s) Name:   Amin Hashemi, Mohammad Bagher Dowlatshahi & Hossein Nezamabadi-pour
Journal name:  International Journal of Machine Learning and Cybernetics
Publisher name:  Springer
Volume Information:  volume 13, pages 49–69 (2022)
Paper Link:   https://link.springer.com/article/10.1007/s13042-021-01347-z