An empirical study of empty prediction of multi-label classification

作者：

Highlights：

• This is the first empirical study of empty prediction of multi-label classification.

• Every algorithm considered all made empty prediction on different datasets.

• HOMER and RAkEL have the overall lowest empty prediction rates in the study.

• Four thresholding methods which in theory can solve empty predictions are compared.

• Probabilistic thresholds are the best solution in terms of example based F1.

摘要

•This is the first empirical study of empty prediction of multi-label classification.•Every algorithm considered all made empty prediction on different datasets.•HOMER and RAkEL have the overall lowest empty prediction rates in the study.•Four thresholding methods which in theory can solve empty predictions are compared.•Probabilistic thresholds are the best solution in terms of example based F1.

论文关键词：Multi-label classification,Empty prediction

论文评审过程：Available online 31 January 2015.

论文官网地址：https://doi.org/10.1016/j.eswa.2015.01.024