A novel multiple instance learning method based on extreme learning machine.

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.

journal_name

Comput Intell Neurosci

authors

Wang J,Cai L,Peng J,Jia Y

doi

10.1155/2015/405890

subject

Has Abstract

pub_date

2015-01-01 00:00:00

pages

405890

eissn

1687-5265

issn

1687-5273

journal_volume

2015

pub_type

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