回归核极限学习机的多标记学习算法

doi:10.16451/j.cnki.issn1003-6059.201805004

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Abstract In the multi-label learning algorithms based on extreme learning machine(ELM), the ELM classification model is often used, and the correlation between labels is ignored. Accordingly, a multi-label learning algorithm of regression kernel extreme learning machine with association rules(ML-ASRKELM) is proposed in this paper. Firstly, the rule vectors between labels are extracted by analyzing the association rules of label space. Then, the prediction results are obtained by the proposed multi-label regression kernel extreme learning machine(ML-RKELM). Eventually, if the rule vectors are not empty, the final results are calculated by the rule vectors and the prediction results of ML-KRELM. Otherwise, the final results are predicted by ML-RKELM. The experimental results show that ML-ASRKELM and ML-RKELM are superior to other algorithms, and the effectiveness of the proposed algorithms are illustrated by the statistical hypothesis test.

Key words： Multi-label Learning Multi-label Learning Extreme Learning Machine(ELM) Extreme Learning Machine(ELM) Label Correlations Label Correlations Association Rules Association Rules Regression Fitting Regression Fitting

Received: 18 December 2017

ZTFLH:

TP 391

Corresponding Authors: CHENG Yusheng, Ph.D., professor. His research interests include big data, rough sets and machine learning for feature selection.

About author:: WANG Yibin, master, professor. His research interests include multi-label learning, machine learning and software security
HE Yue, master student. Her research interests include multi-label learning, machine learning and big data.
PEI Gensheng, master student. His research interests include machine learning, big data and data statistics.

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	WANG Yibin
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	HE Yue
	HE Yue
	PEI Gensheng
	PEI Gensheng

Cite this article:

WANG Yibin,WANG Yibin,CHENG Yusheng等. Multi-label Learning Algorithm of Regression Kernel Extreme Learning Machine[J]. , 2018, 31(5): 419-430.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.201805004 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2018/V31/I5/419

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