An Improved Multiple-Instance Learning Algorithm

Authors:
Fengqing Han;Dacheng Wang;Xiaofeng Liao
Affiliations:
Chongqing Jiaotong University, Chongqing 400074, China and Chongqing University, Chongqing 400044, China;Chongqing Jiaotong University, Chongqing 400074, China;Chongqing University, Chongqing 400044, China
Venue:
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks
Year:
2007

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Abstract

Multiple-instance learning (MIL) is a variation on supervised learning, where the task is to learn a concept given positive and negative bags of instances. In this paper a novel algorithm has been introduced for multiple-instance learning. This method was inspired by both diverse density (DD) and its expectation maximization version (EM-DD). It converts MIL problem to a single-instance setting. This improved method has better accuracy and time complexity than DD and EM-DD. We apply it to drug activity prediction and image retrieval. The experiments show it has competitive accuracy values compared with other previous approaches.