A New Kernel-Based Classification Algorithm for Multi-label Datasets

被引:0
作者
Lahouari Ghouti
机构
[1] King Fahd University of Petroleum and Minerals,Department of Information and Computer Science
来源
Arabian Journal for Science and Engineering | 2016年 / 41卷
关键词
Multi-label learning; Label powerset; Binary relevance; Collaborative representation; Inter-class margin maximization; Multi-label classification; Multi-label datasets;
D O I
暂无
中图分类号
学科分类号
摘要
With the emergence of rich online content, efficient information retrieval systems are required. Application content includes rich text, speech, still images and videos. This content, either stored or queried, can be assigned to many classes or labels at the same time. This calls for the use of multi-label classification techniques. In this paper, a new kernel-based multi-label classification algorithm is proposed. This new classification scheme combines the concepts of class collaborative representation and margin maximization. In multi-label datasets, information content is represented using the collaboration between the existing classes (or labels). Discriminative content representation is achieved by maximizing the inter-class margins. Using public-domain multi-label datasets, the proposed classification solution outperforms its existing counterparts in terms of higher classification accuracy and lower Hamming loss. The attained results confirm the positive effects of discriminative content characterization using class collaboration representation and inter-class margin maximization on the multi-label classification performance.
引用
收藏
页码:759 / 771
页数:12
相关论文
共 55 条
[1]  
Zhang M.(2013)A review on multi-label learning algorithms IEEE Trans. Knowl. Data Eng. 26 1-15
[2]  
Zhou Z.H.(2015)Kernel collaborative face recognition Pattern Recogn. 48 3025-3037
[3]  
Wang D.(2012)On label dependence and loss minimization in multilabel classification Mach. Learn. 88 5-45
[4]  
Lu H.(2008)Multilabel classification via calibrated label ranking Mach. Learn. 73 133-153
[5]  
Yang M.-H.(2011)Random k-label sets for multi-label classification IEEE Trans. Knowl. Data Eng. 23 1079-1089
[6]  
Dembczyński K.(2000)Boostexter: a boosting based system for text categorization Mach. Learn. 39 135-168
[7]  
Cheng W.W.W.(2007)ML-KNN: a lazy learning approach to multi-label learning Pattern Recogn. 40 2038-2048
[8]  
Hüllermeier E.(2006)Multi-label neural networks with applications to functional genomics and text categorization IEEE Trans. Knowl. Data Eng. 18 1338-1351
[9]  
Fürnkranz J.(2009)Combining instance-based learning and logistic regression for multilabel classification Mach. Learn. 76 211-225
[10]  
Hüllermeier E.(2015)Improving kNN multi-label classification in prototype selection scenarios using class proposals Pattern Recogn. 48 1608-1622