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Solving Multiple-Instance Problem: A Lazy Learning Approach

Wang, Jun and Zucker, Jean-Daniel (2000) Solving Multiple-Instance Problem: A Lazy Learning Approach. [Conference Paper]

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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.

Item Type:Conference Paper
Keywords:multiple-instance problem, multiple-instance learning, lazing learning, nearest neighbor
Subjects:Computer Science > Artificial Intelligence
Computer Science > Machine Learning
ID Code:2124
Deposited By:Wang, Jun
Deposited On:10 Mar 2002
Last Modified:11 Mar 2011 08:54

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