Supervised versus multiple instance learning

Abstract

We empirically study the relationship between supervised and multiple instance (MI) learning. Algorithms to learn various concepts have been adapted to the MI representation. However, it is also known that concepts that are PAC-learnable with one-sided noise can be learned from MI data. A relevant question then is: how well do supervised learners do on MI data? We attempt to answer this question by looking at a cross section of MI data sets from various domains coupled with a number of learning algorithms including Diverse Density, Logistic Regression, nonlinear Support Vector Machines and FOIL. We consider a supervised and MI version of each learner. Several interesting conclusions emerge from our work: (1) no MI algorithm is superior across all tested domains, (2) some MI algorithms are consistently superior to their supervised counterparts, (3) using high false-positive costs can improve a supervised learner's performance in MI domains, and (4) in several domains, a supervised algorithm is superior to any MI algorithm we tested.