Single- vs. multiple-instance classification. Issue 9 (September 2015)

• Record Type: Journal Article
• Authors: Alpaydın, Ethem; Cheplygina, Veronika; Loog, Marco; Tax, David M.J.
• Is Part Of: Pattern recognition. Volume 48:Issue 9(2015:Sep.)
• Page Start: 2831
• Page End: 2838
• Publication Date: 2015-09
• Subjects: Classification -- Multiple-instance learning -- Similarity-based representation -- Bioinformatics; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2015.04.006
• Languages: English
• ISSNs: 0031-3203

Abstract

Abstract: 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. Abstract : Highlights: We 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.