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Entropy 2016, 18(6), 225;

Extreme Learning Machine for Multi-Label Classification

School of Information Science and Technology, Northwest University, Xi’an 710069, China
Computer Information Science and Engineering, University of Florida, Gainesville, FL 32608, USA
Department of Computer Science, Xi’an Jiaotong University City College, Xi’an 710069, China
Author to whom correspondence should be addressed.
Academic Editors: Adom Giffin and Kevin H. Knuth
Received: 26 February 2016 / Revised: 21 May 2016 / Accepted: 1 June 2016 / Published: 8 June 2016
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Extreme learning machine (ELM) techniques have received considerable attention in the computational intelligence and machine learning communities because of the significantly low computational time required for training new classifiers. ELM provides solutions for regression, clustering, binary classification, multiclass classifications and so on, but not for multi-label learning. Multi-label learning deals with objects having multiple labels simultaneously, which widely exist in real-world applications. Therefore, a thresholding method-based ELM is proposed in this paper to adapt ELM to multi-label classification, called extreme learning machine for multi-label classification (ELM-ML). ELM-ML outperforms other multi-label classification methods in several standard data sets in most cases, especially for applications which only have a small labeled data set. View Full-Text
Keywords: extreme learning machine; multi-label classification; thresholding strategy extreme learning machine; multi-label classification; thresholding strategy

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Sun, X.; Xu, J.; Jiang, C.; Feng, J.; Chen, S.-S.; He, F. Extreme Learning Machine for Multi-Label Classification. Entropy 2016, 18, 225.

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