TY - JOUR A2 - Natella, Roberto AU - Soheili, Majid AU - Moghadam, Amir-Masoud Eftekhari AU - Dehghan，Mehdi PY - 2020 DA - 2020/09/10 TI -统计分析的性能等级的融合方法应用于均匀整体功能排名SP - 8860044六世- 2020 AB -特征排序的子类别特征选择是一个重要的预处理技术,排名数据集的所有特性,许多重要的功能denote a lot of information. The ensemble learning has two advantages. First, it has been based on the assumption that combining different model’s output can lead to a better outcome than the output of any individual models. Second, scalability is an intrinsic characteristic that is so crucial in coping with a large scale dataset. In this paper, a homogeneous ensemble feature ranking algorithm is considered, and the nine rank fusion methods used in this algorithm are analyzed comparatively. The experimental studies are performed on real six medium datasets, and the area under the feature-forward-addition curve criterion is assessed. Finally, the statistical analysis by repeated-measures analysis of variance results reveals that there is no big difference in the performance of the rank fusion methods applied in a homogeneous ensemble feature ranking; however, this difference is a statistical significance, and the B-Min method has a little better performance. SN - 1058-9244 UR - https://doi.org/10.1155/2020/8860044 DO - 10.1155/2020/8860044 JF - Scientific Programming PB - Hindawi KW - ER -