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ELM-MC: multi-label classification framework based on extreme learning machine
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DOI：10.1007/s13042-020-01114-6.png)
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摘要
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Multi-label classification methods aim to a class of application problems where each individual contains a single instance while associates with a set of labels simultaneously. In this paper, we formulate a novel multi-label classification method based on extreme learning machine framework, named ELM-MC algorithm. The essence of ELM-MC algorithm is to convert the multi-label classification problem into some single-label classifications, and fully considers the relationship among different labels. After the classification of one label, the associations with next label are applied to update the learning parameters in ELM-MC algorithm. In addition, we design a backup pool for the hidden nodes. It can help to select relatively suitable hidden nodes to the corresponding label classification case. In the simulation part, six famous databases are applied to demonstrate the satisfied classification accuracy of the proposed method.
Keyword:
Multi-label classification
Extreme learning machine
Principle component analysis
Linear discriminant analysis
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