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MILIS: multiple instance learning with instance selection

journal contribution
posted on 2011-05-01, 00:00 authored by Zhouyu Fu, Antonio Robles-KellyAntonio Robles-Kelly, Jun Zhou
Multiple instance learning (MIL) is a paradigm in supervised learning that deals with the classification of collections of instances called bags. Each bag contains a number of instances from which features are extracted. The complexity of MIL is largely dependent on the number of instances in the training data set. Since we are usually confronted with a large instance space even for moderately sized real-world data sets applications, it is important to design efficient instance selection techniques to speed up the training process without compromising the performance. In this paper, we address the issue of instance selection in MIL. We propose MILIS, a novel MIL algorithm based on adaptive instance selection. We do this in an alternating optimization framework by intertwining the steps of instance selection and classifier learning in an iterative manner which is guaranteed to converge. Initial instance selection is achieved by a simple yet effective kernel density estimator on the negative instances. Experimental results demonstrate the utility and efficiency of the proposed approach as compared to the state of the art.

History

Journal

IEEE transactions on pattern analysis and machine intelligence

Volume

33

Pagination

958-977

Location

Piscataway, N.J.

ISSN

0162-8828

Language

eng

Publication classification

C1.1 Refereed article in a scholarly journal

Copyright notice

2011, IEEE

Issue

5

Publisher

Institute of Electrical and Electronics Engineers