Multiple-Instance Learning of Real-Valued Data

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- k NN, 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.

Authors

• Daniel R. Dooly
• Qi Zhang School of Computer Science, Fudan University, Shanghai, China Key Laboratory of Intelligent Information Processing, Fudan University, Shanghai, China
• Sally A. Goldman
• Robert A. Amar

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