05814603

a

05814603 Foulds, James R. Frank, Eibe Speeding up and boosting diverse density learning. Pfahringer, Bernhard (ed.) et al., Discovery science. 13th international conference, DS 2010, Canberra, Australia, October 6--8, 2010. Proceedings. Berlin: Springer (ISBN 978-3-642-16183-4/pbk). Lecture Notes in Computer Science 6332. Lecture Notes in Artificial Intelligence, 102-116 (2010). 2010 Berlin: Springer EN

doi:10.1007/978-3-642-16184-1_8

Summary: In 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.