Threshold optimization for F measure of macro-averaged precision and recall
作者：
Highlights：
• Coordinate-wise maximum for the analyzed measure may not be maximum in usual sense.
• The proposed fixed point method can localize all maximums of the measure.
• The method works the more precise, the bigger class count is.
• The approach is successfully applied to the real-world datasets.
• The difference from similar macro F measure is investigated.
摘要
•Coordinate-wise maximum for the analyzed measure may not be maximum in usual sense.•The proposed fixed point method can localize all maximums of the measure.•The method works the more precise, the bigger class count is.•The approach is successfully applied to the real-world datasets.•The difference from similar macro F measure is investigated.
论文关键词：Macro-averaged F measure,Multi-label classification,Optimal threshold selection,Fixed point method
论文评审过程：Received 29 November 2018, Revised 17 January 2020, Accepted 26 January 2020, Available online 27 January 2020, Version of Record 4 February 2020.
论文官网地址：https://doi.org/10.1016/j.patcog.2020.107250
