MILES: Multiple-Instance Learning via Embedded Instance Selection

Yixin Chen
University of Mississippi

Jinbo Bi
Siemens Medical Solutions

James Z. Wang
The Pennsylvania State University
Abstract:

Multiple-instance problems arise from the situations where training class labels are attached to sets of samples (named {\em bags}), instead of individual samples within each bag (called {\em instances}). Most previous multiple-instance learning (MIL) algorithms are developed based on the assumption that a bag is positive if and only if at least one of its instances is positive. Although the assumption works well in a drug activity prediction problem, it is rather restrictive for other applications, especially those in the computer vision area. We propose a learning method, MILES (Multiple-Instance Learning via Embedded instance Selection), which converts the multiple-instance learning problem to a standard supervised learning problem that does not impose the assumption relating instance labels to bag labels. MILES maps each bag into a feature space defined by the instances in the training bags via an instance similarity measure. This feature mapping often provides a large number of redundant or irrelevant features. Hence 1-norm SVM is applied to select important features as well as construct classifiers simultaneously. We have performed extensive experiments. In comparison with other methods, MILES demonstrates competitive classification accuracy, high computation efficiency, and robustness to labeling uncertainty.


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Citation: Yixin Chen, Jinbo Bi and James Z. Wang, ``MILES: Multiple-Instance Learning via Embedded Instance Selection,'' IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 28, no. 12, pp. 1931-1947, 2006.

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Last Modified: May 12, 2006
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