Multiple instance ranking

ABSTRACT

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 from the hydrogen abstraction problem in computational chemistry, that of predicting the group of hydrogen atoms from which a hydrogen 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 preferred 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.

REFERENCES

• 1: Afzelius, L., Arnby, C. H., Broo, A., Carlsson, L., Isaksson, C., Jurva, U., Kjellander, B., Kolmodin, K., Nilsson, K., Raubacher, F., & Weidolf, L. (2007). State-of-the-art tools for computational site of metabolism predictions: Comparative analysis, mechanistical insights, and future applications. Drug Metabolism Reviews, 39, 61--86.
• 2: Andrews, S., Tsochantaridis, I., & Hofmann, T. (2003). Support vector machines for multiple-instance learning. Advances in Neural Information Processing Systems 15.
• 3: Thomas G. Dietterich , Richard H. Lathrop , Tomás Lozano-Pérez, Solving the multiple instance problem with axis-parallel rectangles, Artificial Intelligence, v.89 n.1-2, p.31-71, Jan. 1997 [doi>10.1016/S0004-3702(96)00034-3]
• 4: Guengerich, F. P. (1999). Cytochrome p-450 3A4: Regulation and role in drug metabolism. Annual Review of Pharmacology and Toxicology, 39, 1--7.
• 5: Thorsten Joachims, Optimizing search engines using clickthrough data, Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining, July 23-26, 2002, Edmonton, Alberta, Canada [doi>10.1145/775047.775067]
• 6: Mangasarian, O. L., & Wild, E. W. (2008). Multiple instance classification via successive linear programming. Journal of Optimization Theory and Applications, Accepted.
• 7: Oded Maron , Aparna Lakshmi Ratan, Multiple-Instance Learning for Natural Scene Classification, Proceedings of the Fifteenth International Conference on Machine Learning, p.341-349, July 24-27, 1998
• 8: Joseph F. Murray , Gordon F. Hughes , Kenneth Kreutz-Delgado, Machine Learning Methods for Predicting Failures in Hard Drives: A Multiple-Instance Application, The Journal of Machine Learning Research, 6, p.783-816, 12/1/2005
• 9: Ramon, J., & Raedt, L. D. (2000). Multi instance neural networks. Proceedings of the 17th International Machine Learning Conference.
• 10: Soumya Ray , David Page, Multiple Instance Regression, Proceedings of the Eighteenth International Conference on Machine Learning, p.425-432, June 28-July 01, 2001
• 11: Rendic, S. (1997). Summary of information on human CYP enzymes: human P450 metabolism data. Drug Metabolism Reviews, 34, 83--448.
• 12: John Shawe-Taylor , Nello Cristianini, Kernel Methods for Pattern Analysis, Cambridge University Press, New York, NY, 2004
• 13: Sheridan, R. P., Korzekwa, K. R., Torres, R. A., & Walker, M. J. (2007). Empirical regioselectivity models for human cytochromes P450 3A4, 2D6, and 2C9. Journal of Medicinal Chemistry, 50, 3173--3184.
• 14: Singh, S. B., Shen, L. Q., Walker, M. J., & Sheridan, R. P. (2003). A model for predicting likely sites of CYP3A4-mediated metabolism on drug-like molecules. Journal of Medicinal Chemistry, 46, 1330--1336.
• 15: Thummel, K. E., Kunzea, K. L., & Shen, D. D. (1997). Enzyme-catalyzed processes of first-pass hepatic and intestinal drug extraction. Advanced Drug Delivery Reviews, 27, 99--127.
• 16: Zhang, Q., & Goldman, S. A. (2001). EM-DD: An improved multiple-instance learning technique. Advances in Neural Information Processing Systems 14.

INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
      I.2.6 Learning

Additional Classification:
  G. Mathematics of Computing
  G.1 NUMERICAL ANALYSIS
      G.1.6 Optimization
          Subjects: Linear programming

  I. Computing Methodologies
  I.5 PATTERN RECOGNITION
      I.5.2 Design Methodology
          Subjects: Classifier design and evaluation
      I.5.3 Clustering
          Subjects: Algorithms
  I.6 SIMULATION AND MODELING
      I.6.4 Model Validation and Analysis

General Terms:
Algorithms, Design, Performance, Theory