creators_name: Wang, Jun
creators_name: Zucker, Jean-Daniel
editors_name: Langley, Pat
type: confpaper
datestamp: 2002-03-10
lastmod: 2011-03-11 08:54:54
metadata_visibility: show
title: Solving Multiple-Instance Problem: A Lazy Learning Approach
ispublished: pub
subjects: comp-sci-art-intel
subjects: comp-sci-mach-learn
full_text_status: public
keywords: multiple-instance problem,
multiple-instance learning,
lazing learning,
nearest neighbor

abstract: As opposed to traditional supervised learning, multiple-instance learning 
    concerns the problem of classifying a bag of instances, given bags that are 
    labeled by a teacher as being overall positive or negative. Current research 
    mainly concentrates on adapting traditional concept learning to solve this 
    problem. In this paper we investigate the use of lazy learning and Hausdorff 
    distance to approach the multiple-instance problem. We present two variants of 
    the K-nearest neighbor algorithm, called Bayesian-KNN and Citation-KNN, solving 
    the multiple-instance problem. Experiments on the Drug discovery benchmark data 
    show that both algorithms are competitive with the best ones conceived in the 
    concept learning framework. Further work includes exploring of a combination of 
    lazy and eager multiple-instance problem classifiers.
date: 2000
date_type: published
publisher: Morgan Kaufmann
pagerange: 1119-1125
refereed: TRUE
referencetext: Aha, D. W. (Ed.). (1997). Lazy Learning. Dordrecht, The Netherlands: Kluwer Academic Publishers.
Auer, P. (1997). On learning from multi-instance examples: Empirical evaluation of a theoretical approach. Proceedings of the Fourteenth International Conference on Machine Learning (pp. 21-29). San Francisco: Morgan Kaufmann.
Bergadano, F., Giordana, A., & Saitta, L. (1991). Machine learning: An integrated framework and its application. Chichester, UK: Ellis Horwood.
Blockeel, H., & De Raedt, L. (1998). Top-down induction of first order logical decision trees. Artificial Intelligence, 101, 285-297.
Blum, A., & Kalai, A. (1998). A note on learning from multiple-instance examples. Machine Learning, 30, 23-29.
Bottou, P., & Vapnik, V. (1992). Local learning algorithms. Neural Computation, 4, 888-900.
Dasarathy, B.V. (1991). Nearest neighbor norms: NN pattern classification techniques. Los Alamitos, CA: IEEE Computer Society Press.
De Raedt, L. (1998). Attribute-value learning versus inductive logic programming: The missing links. Proceedings of the Eighth International Conference on Inductive Logic Programming (pp. 1-8). Springer-Verlag.
Dietterich, T.G., Jain, A., Lathrop, R. H., & Lozano-Pérez, T. (1994). A comparison of dynamic reposing and tangent distance for drug activity prediction. Advances in Neural Information Processing Systems, 6, 216-223. San Mateo: Morgan Kaufmann.
Dietterich, T.G., Lathrop, R. H., & Lozano-Pérez, T. (1997). Solving the multiple-instance problem with axis-parallel rectangles. Artificial Intelligence, 89, 31-71. 
Edgar, G.A. (1995). Measure, topology, and fractal geometry (3rd print). Springer-Verlag.
Garfield, E. (1979). Citation indexing: Its theory and application in science, technology, and humanities. New York: John Wiley & Sons.
Long, P.M., & Tan, L. (1996). PAC-learning axis aligned rectangles with respect to product distributions from multiple-instance examples. Proceedings of the Ninth Annual Conference on Computational Learning Theory (pp. 228-234). New York: ACM Press.
Maron. O., & Lozano-Pérez, T. (1998). A framework for multiple-instance learning. Advances in Neural Information Processing Systems, 10. MIT Press. 
Maron, O., & Ratan, A. L. (1998). Multiple-instance learning for natural scene classification. Proceedings of the Fifteenth International Conference on Machine Learning. San Francisco: Morgan Kaufmann.
Maron, O. (1998). Learning from ambiguity. Doctoral dissertation, Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology. 
Perny, P. (1998). Multicriteria filtering methods based on concordance and non-discordance principles. Annals of Operations Research, 80, 137-165. Bussum, The Netherlands: Baltzer Science Publishers.
Ruffo, G. (2000). Learning single and multiple instance decision tree for computer security applications. Doctoral dissertation, Department of Computer Science, University of Turin, Torino, Italy.
Sebag, M., & Rouveirol, C. (1997). Tractable induction and classification in first order logic via stochastic matching. Proceedings of the Fifteenth International Joint Conference on Artificial Intelligence (pp. 888-893). Nagoya, Japan: Morgan Kaufmann.
Vapnik, V. (1995). The nature of statistical learning theory. New York: Springer-Verlag.
Zucker, J.-D., & Ganascia, J.-G. (1996). Changes of representation for efficient learning in structural domains. Proceedings of the Thirteenth International Conference on Machine Learning (pp. 543-551). Bary, Italy: Morgan Kaufmann.
Zucker, J.-D., & Ganascia, J.-G. (1998). Learning structurally indeterminate clauses. Proceedings of the Eighth International Conference on Inductive Logic Programming (pp. 235-244). Springer-Verlag.

citation:   Wang, Jun and Zucker, Jean-Daniel  (2000) Solving Multiple-Instance Problem: A Lazy Learning Approach.  [Conference Paper]     
document_url: http://cogprints.org/2124/3/wang_ICML2000.pdf