Improving Multi-Instance Multi-Label Learning by Extreme Learning Machine
Source abstract
Abstract
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.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Machine Learning and ELM · Text and Document Classification Technologies · Domain Adaptation and Few-Shot Learning
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
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
References (52)
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