A new k-harmonic nearest neighbor classifier based on the multi-local means
作者：
Highlights：
• K different multi-local means based on the k-nearest neighbors are employed.
• The harmonic mean distance is firstly introduced in the KNN classification problems.
• The classification error rates can be significantly reduced.
• Less sensitive to the choice of neighborhood size k.
• Easily designed in practice with a little extra computation complexity.
摘要
•K different multi-local means based on the k-nearest neighbors are employed.•The harmonic mean distance is firstly introduced in the KNN classification problems.•The classification error rates can be significantly reduced.•Less sensitive to the choice of neighborhood size k.•Easily designed in practice with a little extra computation complexity.
论文关键词：Local mean,k-nearest neighbor,Harmonic mean distance,Pattern classification,Small training sample size
论文评审过程：Received 22 June 2016, Revised 18 September 2016, Accepted 19 September 2016, Available online 20 September 2016, Version of Record 29 September 2016.
论文官网地址：https://doi.org/10.1016/j.eswa.2016.09.031
