Online multi-label learning with cost-sensitive budgeted SVM. (2018) - Liu, Jing; Guo, Zhongwen; Jian, Ling; Qiu, Like; Wang, Xupeng
Multi-label learning deals with data associated with multiple labels simultaneously. It has been extensively studied in diverse areas such as information retrieval, bioinformatics, image annotation, etc. Explosive growth of multi-label-related data has brought challenges of how to efficiently learn these labelled data and automatically label the unlabelled data. In this paper, we propose an online learning algorithm which processes the data arriving in streaming fashion. It is space-saving and scalable to large-scale problems. Specifically, to tackle the class imbalance problem, we exploit label prior to construct cost-sensitive function for sub-classification problem. Experimental studies corroborate the performance of our approaches on datasets drawn from diverse domains and demonstrate that our proposed algorithm is an ideal candidate to process streaming data and deal with online multi-label learning tasks.