文章基本信息

标题：Optimal Feature Aggregation and Combination for Two-Dimensional Ensemble Feature Selection
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作者：Machmud Roby Alhamidi ; Wisnu Jatmiko
期刊名称：Information
电子版ISSN：2078-2489
出版年度：2020
卷号：11
期号：1
DOI：10.3390/info11010038
出版社：MDPI Publishing
摘要：Feature selection is a way of reducing the features of data such that, when the classification algorithm runs, it produces better accuracy. In general, conventional feature selection is quite unstable when faced with changing data characteristics. It would be inefficient to implement individual feature selection in some cases. Ensemble feature selection exists to overcome this problem. However, with the advantages of ensemble feature selection, some issues like stability, threshold, and feature aggregation still need to be overcome. We propose a new framework to deal with stability and feature aggregation. We also used an automatic threshold to see whether it was efficient or not; the results showed that the proposed method always produces the best performance in both accuracy and feature reduction. The accuracy comparison between the proposed method and other methods was 0.5–14% and reduced more features than other methods by 50%. The stability of the proposed method was also excellent, with an average of 0.9. However, when we applied the automatic threshold, there was no beneficial improvement compared to without an automatic threshold. Overall, the proposed method presented excellent performance compared to previous work and standard ReliefF..
关键词：ensemble feature selection; stability; feature aggregation; threshold ensemble feature selection ; stability ; feature aggregation ; threshold