Robust Multiple-Instance Learning with Superbags

Abstract

Multiple-instance learning consists of two alternating optimization steps: learning a classifier with missing labels and finding the missing labels with the classifier. These steps are iteratively performed on the same training data, thus imputing labels by evaluating the classifier on the data it is trained upon. Consequently this alternating optimization is prone to self-amplification and overfitting. To resolve this crucial issue of popular multiple-instance learning we propose to establish a random ensemble of sets of bags, i.e., superbags. Classifier training and label inference are then decoupled by performing them on different superbags. Label inference is performed on samples from separate superbags, and thus avoids label imputation on training samples in the same superbag. Experimental evaluations on standard datasets show consistent improvement over widely used approaches for multiple-instance learning.