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Computational Intelligence and Neuroscience
Volume 2015, Article ID 405890, 6 pages
http://dx.doi.org/10.1155/2015/405890
Research Article

A Novel Multiple Instance Learning Method Based on Extreme Learning Machine

School of Electrical Engineering, Zhengzhou University, Zhengzhou 450001, China

Received 18 December 2014; Revised 18 January 2015; Accepted 18 January 2015

Academic Editor: Thomas DeMarse

Copyright © 2015 Jie Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Since real-world data sets usually contain large instances, it is meaningful to develop efficient and effective multiple instance learning (MIL) algorithm. As a learning paradigm, MIL is different from traditional supervised learning that handles the classification of bags comprising unlabeled instances. In this paper, a novel efficient method based on extreme learning machine (ELM) is proposed to address MIL problem. First, the most qualified instance is selected in each bag through a single hidden layer feedforward network (SLFN) whose input and output weights are both initialed randomly, and the single selected instance is used to represent every bag. Second, the modified ELM model is trained by using the selected instances to update the output weights. Experiments on several benchmark data sets and multiple instance regression data sets show that the ELM-MIL achieves good performance; moreover, it runs several times or even hundreds of times faster than other similar MIL algorithms.