Distance-based online classifiers
作者：
Highlights：
• Proposing the online distance-based classifier with fuzzy C-means clustering.
• Evaluating different strategies for online updating training dataset.
• Adopting Rotation Forest method to extend the online distance-based classifier.
• Analysis of the computational complexity of the proposed algorithms.
• Extensive computational experiment validating the proposed algorithms.
摘要
•Proposing the online distance-based classifier with fuzzy C-means clustering.•Evaluating different strategies for online updating training dataset.•Adopting Rotation Forest method to extend the online distance-based classifier.•Analysis of the computational complexity of the proposed algorithms.•Extensive computational experiment validating the proposed algorithms.
论文关键词：Online learning,Fuzzy C-means clustering,Kernelized clustering,Rotation forest
论文评审过程：Received 27 July 2015, Revised 8 May 2016, Accepted 9 May 2016, Available online 9 May 2016, Version of Record 19 May 2016.
论文官网地址：https://doi.org/10.1016/j.eswa.2016.05.015
