Parallel and Sequential Support Vector Machines for Multi-label Classification
Liwei Wang, Ming Qi Chang, Jufu Feng · 2005
Abstract. Multi-label classification is the problem that classes are not mutually exclusive, so that an example may belong to more than one category. Multi-label classification arises typically in semantic scene clas-sification, text categorization, medical diagnosis, and bioinformatics. In this paper, we propose two algorithms, called Parallel Support Vector Machines (PSVMs) and Sequential Support Vector Machines (SSVMs), to handle multi-label classification problems. We applied them to scene classification. It is demonstrated that PSVM is comparable to, and SSVM outperforms the so-called cross-training C-criterion method. Keywords:Multi-label classification; Scene Classification; Parallel SVM; Sequential SVM; Cross-training; C-criterion testing