A generalized weighted distance k-Nearest Neighbor for multi-label problems
作者：
Highlights：
• Using a gradient descent method for prototype weighting in nearest neighbor.
• Generalizing the method to supportk nearest neighbor (k>=1).
• Generalizing the method to support all elements of confusion matrix.
• Using the method in binary relevance approach of multi-label problems.
• Dealing with imbalanced data problem.
摘要
•Using a gradient descent method for prototype weighting in nearest neighbor.•Generalizing the method to supportk nearest neighbor (k>=1).•Generalizing the method to support all elements of confusion matrix.•Using the method in binary relevance approach of multi-label problems.•Dealing with imbalanced data problem.
论文关键词：Multi-label classification,Binary relevance,Nearest neighbor,Adaptive distance measure,Prototype weighting
论文评审过程：Received 19 January 2020, Revised 24 May 2020, Accepted 30 June 2020, Available online 1 July 2020, Version of Record 9 February 2021.
论文官网地址：https://doi.org/10.1016/j.patcog.2020.107526
