Journal of Systems Engineering and Electronics ›› 2014, Vol. 25 ›› Issue (3): 502-513.doi: 10.1109/JSEE.2014.00058

• SOFTWARE ALGORITHM AND SIMULATION • Previous Articles     Next Articles

Multi-label dimensionality reduction and classification with extreme learning machines

Lin Feng1,2, Jing Wang1,2, Shenglan Liu1,2,*, and Yao Xiao1,2   

  1. 1. Faculty of Electronic Information and Electrical Engineering, School of Computer Science and Technology, Dalian University of Technology, Dalian 116024, China;
    2. School of Innovation Experiment, Dalian University of Technology, Dalian 116024, China
  • Online:2014-07-01 Published:2010-01-03


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.