Robust multiple-instance learning ensembles using random subspace instance selection

作者：

Highlights：

•

摘要

Highlights•A new method, Random Subspace Instance Selection, is proposed to design MIL ensembles.•The method yields ensembles that are robust to variations of witness rate, data distributions and noise.•The method yields state-of-the-art results on several benchmark data sets.

论文关键词：Multiple-instance learning,Random subspace methods,Classifier ensembles,Instance selection,Weakly supervised learning,Classification,MIL

论文评审过程：Author links open overlay panelMarc-AndréCarbonneauabEricGrangerbAlexandre J.RaymondaGhyslainGagnona

论文官网地址：https://doi.org/10.1016/j.patcog.2016.03.035