Multiple-Instance Learning of Real-Valued Data

Daniel R. Dooly, Qi Zhang, Sally A. Goldman, Robert A. Amar; 3(Dec):651-678, 2002.


The multiple-instance learning model has received much attention recently with a primary application area being that of drug activity prediction. Most prior work on multiple-instance learning has been for concept learning, yet for drug activity prediction, the label is a real-valued affinity measurement giving the binding strength. We present extensions of k-nearest neighbors (k-NN), Citation-kNN, and the diverse density algorithm for the real-valued setting and study their performance on Boolean and real-valued data. We also provide a method for generating chemically realistic artificial data.

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