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Solving the multiple instance problem with axis-parallel rectangles

TitleSolving the multiple instance problem with axis-parallel rectangles
Publication TypeJournal Article
Year of Publication1997
AuthorsDietterich, T. G., R. H. Lathrop, and T. Lozano-Pérez
JournalArtificial Intelligence
Volume89
Issue1-2
Pagination31 - 71
Date Published01/1997
ISSN00043702
Keywordsdrug design, machine learning, structure-activity relationships
Abstract

The multiple instance problem arises in tasks where the training examples are ambiguous: a single example object may have many alternative feature vectors (instances) that describe it, and yet only one of those feature vectors may be responsible for the observed classification of the object. This paper describes and compares three kinds of algorithms that learn axis-parallel rectangles to solve the multiple instance problem. Algorithms that ignore the multiple instance problem perform very poorly. An algorithm that directly confronts the multiple instance problem (by attempting to identify which feature vectors are responsible for the observed classifications) performs best, giving 89% correct predictions on a musk odor prediction task. The paper also illustrates the use of artificial data to debug and compare these algorithms.

DOI10.1016/S0004-3702(96)00034-3
Short TitleArtificial Intelligence