2002-03-10Z2011-03-11T08:54:54Zhttp://cogprints.org/id/eprint/2124This item is in the repository with the URL: http://cogprints.org/id/eprint/21242002-03-10ZSolving Multiple-Instance Problem: A Lazy Learning ApproachAs opposed to traditional supervised learning, multiple-instance learning
concerns the problem of classifying a bag of instances, given bags that are
labeled by a teacher as being overall positive or negative. Current research
mainly concentrates on adapting traditional concept learning to solve this
problem. In this paper we investigate the use of lazy learning and Hausdorff
distance to approach the multiple-instance problem. We present two variants of
the K-nearest neighbor algorithm, called Bayesian-KNN and Citation-KNN, solving
the multiple-instance problem. Experiments on the Drug discovery benchmark data
show that both algorithms are competitive with the best ones conceived in the
concept learning framework. Further work includes exploring of a combination of
lazy and eager multiple-instance problem classifiers.Jun WangJean-Daniel Zucker