Text feature selection with a robust weight scheme and dynamic dimension reduction to text document clustering
作者：
Highlights：
• Three meta-heuristic algorithms are adapted to solve the feature selection problem.
• Feature selection methods are established based on a novel weighting scheme.
• Dimension reduction technique is proposed to reduce the number of features.
• K-mean clustering algorithm is used based on the features obtained.
• The proposed methods outperform the comparative methods.
摘要
•Three meta-heuristic algorithms are adapted to solve the feature selection problem.•Feature selection methods are established based on a novel weighting scheme.•Dimension reduction technique is proposed to reduce the number of features.•K-mean clustering algorithm is used based on the features obtained.•The proposed methods outperform the comparative methods.
论文关键词：Feature selection,Dynamic dimension reduction,Text document clustering,Weight score,Metaheuristics
论文评审过程：Received 18 December 2016, Revised 2 May 2017, Accepted 3 May 2017, Available online 3 May 2017, Version of Record 8 May 2017.
论文官网地址：https://doi.org/10.1016/j.eswa.2017.05.002
