Foulds, James RichardFrank, Eibe2010-12-092010-12-092010Foulds, J.R. & Frank, E. (2010). Speeding up and boosting diverse density learning. In B. Pfahringer, G. Holmes & A. Hoffmann (Eds.), LNAI 6332, Discovery Science, Proceedings of 13th International Conferecne, DS2010, Canberra, Australia, October 6-8 2010 (pp. 102-116). Berlin, Germany: Springer.https://hdl.handle.net/10289/4865In multi-instance learning, each example is described by a bag of instances instead of a single feature vector. In this paper, we revisit the idea of performing multi-instance classification based on a point-and-scaling concept by searching for the point in instance space with the highest diverse density. This is a computationally expensive process, and we describe several heuristics designed to improve runtime. Our results show that simple variants of existing algorithms can be used to find diverse density maxima more efficiently. We also show how significant increases in accuracy can be obtained by applying a boosting algorithm with a modified version of the diverse density algorithm as the weak learner.enThis is an author’s accepted version of a conference paper published in the Proceedings of the 13th International Conference on Discovery Science. © 2010 Springer.computer sciencedata miningmulti-instance learningMachine learningConference Contribution10.1007/978-3-642-16184-1_8