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论文类型：期刊论文

发表时间：2014-06-01

发表刊物：JOURNAL OF SYSTEMS ENGINEERING AND ELECTRONICS

收录刊物：SCIE、EI、Scopus

卷号：25

期号：3

页面范围：502-513

ISSN号：1004-4132

关键字：multi-label; dimensionality reduction; kernel trick; classification

摘要：In the need of some real applications, such as text categorization and image classification, the multi-label learning gradually becomes a hot research point in recent years. Much attention has been paid to the research of multi-label classification algorithms. Considering the fact that the high dimensionality of the multi-label datasets may cause the curse of dimensionality and will hamper the classification process, a dimensionality reduction algorithm, named multi-label kernel discriminant analysis (MLKDA), is proposed to reduce the dimensionality of multi-label datasets. MLKDA, with the kernel trick, processes the multi-label integrally and realizes the nonlinear dimensionality reduction with the idea similar with linear discriminant analysis (LDA). In the classification process of multi-label data, the extreme learning machine (ELM) is an efficient algorithm in the premise of good accuracy. MLKDA, combined with ELM, shows a good performance in multi-label learning experiments with several datasets. The experiments on both static data and data stream show that MLKDA outperforms multi-label dimensionality reduction via dependence maximization (MDDM) and multi-label linear discriminant analysis (MLDA) in cases of balanced datasets and stronger correlation between tags, and ELM is also a good choice for multi-label classification.