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Appl. Sci. 2016, 6(6), 160;

Improving Multi-Instance Multi-Label Learning by Extreme Learning Machine

College of Computer Science and Engineer, Northeastern University, Shenyang 110819, China
Key Laboratory of Computer Network and Information Integration, Southeast University, Ministry of Education, Nanjing 211189, China
Author to whom correspondence should be addressed.
Academic Editor: Christian Dawson
Received: 15 December 2015 / Revised: 7 May 2016 / Accepted: 10 May 2016 / Published: 24 May 2016
(This article belongs to the Special Issue Applied Artificial Neural Network)
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Multi-instance multi-label learning is a learning framework, where every object is represented by a bag of instances and associated with multiple labels simultaneously. The existing degeneration strategy-based methods often suffer from some common drawbacks: (1) the user-specific parameter for the number of clusters may incur the effective problem; (2) SVM may bring a high computational cost when utilized as the classifier builder. In this paper, we propose an algorithm, namely multi-instance multi-label (MIML)-extreme learning machine (ELM), to address the problems. To our best knowledge, we are the first to utilize ELM in the MIML problem and to conduct the comparison of ELM and SVM on MIML. Extensive experiments have been conducted on real datasets and synthetic datasets. The results show that MIMLELM tends to achieve better generalization performance at a higher learning speed. View Full-Text
Keywords: multi-instance multi-label; extreme learning machine; genetic algorithm multi-instance multi-label; extreme learning machine; genetic algorithm

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Yin, Y.; Zhao, Y.; Li, C.; Zhang, B. Improving Multi-Instance Multi-Label Learning by Extreme Learning Machine. Appl. Sci. 2016, 6, 160.

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