Graph based feature selection investigating boundary region of rough set for language identification
作者：
Highlights：
• Suitable features are extracted based on the nature of speech.
• Positive region of RST can measure the similarity if the given information is precise.
• Boundary region of RST is also explored as audio speech dataset may contain uncertainty.
• A MST is generated and a feature selection method is devised to select the relevant features.
• The method is compared with popular diverse feature selection algorithms.
摘要
•Suitable features are extracted based on the nature of speech.•Positive region of RST can measure the similarity if the given information is precise.•Boundary region of RST is also explored as audio speech dataset may contain uncertainty.•A MST is generated and a feature selection method is devised to select the relevant features.•The method is compared with popular diverse feature selection algorithms.
论文关键词：Language identification,Feature selection,Relative indiscernibility relation,Attribute dependency,Boundary region exploration
论文评审过程：Received 30 July 2019, Revised 3 May 2020, Accepted 15 May 2020, Available online 29 May 2020, Version of Record 23 June 2020.
论文官网地址：https://doi.org/10.1016/j.eswa.2020.113575
