An evolutionary Pentagon Support Vector finder method
作者：
Highlights：
• Detecting outliers is vital in data mining and classification algorithms.
• In this sense, we propose an evolutionary Pentagon Support Vector finder method.
• We use geometrical calculations and evolutionary clustering to make a more effective system.
• Our proposed approach successfully removes outliers from all data sets.
• We do not lose vital samples and do not harm final accuracy.
摘要
•Detecting outliers is vital in data mining and classification algorithms.•In this sense, we propose an evolutionary Pentagon Support Vector finder method.•We use geometrical calculations and evolutionary clustering to make a more effective system.•Our proposed approach successfully removes outliers from all data sets.•We do not lose vital samples and do not harm final accuracy.
论文关键词：Big data,Data mining,Support vector,Artificial Bee Colony (ABC),Evolutionary clustering,Fuzzy C means (FCM),Pentagon Support Vector finder (PSV)
论文评审过程：Received 22 October 2018, Revised 11 December 2019, Accepted 5 February 2020, Available online 7 February 2020, Version of Record 20 February 2020.
论文官网地址：https://doi.org/10.1016/j.eswa.2020.113284
