Single- vs. multiple-instance classification

In multiple-instance (MI) classification, each input object or event is represented by a set of instances, named a bag, and it is the bag that carries a label. MI learning is used in different applications where data is formed in terms of such bags and where individual instances in a bag do not have a label. We review MI classification from the point of view of label information carried in the instances in a bag, that is, their sufficiency for classification. Our aim is to contrast MI with the standard approach of single-instance (SI) classification to determine when casting a problem in the MI framework is preferable. We compare instance-level classification, combination by noisy-or, and bag-level classification, using the support vector machine as the base classifier. We define a set of synthetic MI tasks at different complexities to benchmark different MI approaches. Our experiments on these and two real-world bioinformatics applications on gene expression and text categorization indicate that depending on the situation, a different decision mechanism, at the instance- or bag-level, may be appropriate. If the instances in a bag provide complementary information, a bag-level MI approach is useful; but sometimes the bag information carries no useful information at all and an instance-level SI classifier works equally well, or better. HighlightsWe categorize problems by the amount of label information instances in a bag carry.We define synthetic tasks of increasing complexity or intra-bag dependency.These problems allow us to measure the power of multiple-instance algorithms.We experiment on two bioinformatics data for gene expression and text categorization.