Multiple instance ranking

This paper introduces a novel machine learning model called multiple instance ranking (MIRank) that enables ranking to be performed in a multiple instance learning setting. The motivation for MIRank stems hydrogen abstraction problem in computational chemistry, that of predicting the group of hydrogen atoms from which en is abstracted (removed) during metabolism. The model predicts the preferred hydrogen group within a molecule by ranking the groups, with the ambiguity of not knowing which hydrogen atom within the d group is actually abstracted. This paper formulates MIRank in its general context and proposes an algorithm for solving MIRank problems using successive linear programming. The method outperforms multiple instance classification models on several real and synthetic datasets.

• Type: Conference
• Year: 2008
• Where: ICML
• Authors: Charles Bergeron, Jed Zaretzki, Curt M. Breneman, Kristin P. Bennett
• Keywords: Hydrogen Abstraction Problem, ICML 2008, Machine Learning, Multiple Instance Classification, Preferred Hydrogen Group