Online multi-label learning is an efficient classification paradigm in machine learning. However, traditional online multi-label methods often need requesting all class labels of each incoming sample, which is often human cost and time-consuming in labeling classification problem. In order to tackle these problems, in this paper, we present online multi-label passive aggressive active (MLPAA) learning algorithm by combining binary relevance (BR) decomposition strategy with online passive aggressive active (PAA) method. The proposed MLPAA algorithm not only uses the misclassified labels to update the classifier, but also exploits correctly classified examples with low prediction confidence. We perform extensive experimental comparison for our algorithm and the other methods using nine benchmark data sets. The encouraging results of our experiments validate the effectiveness of our proposed method.
CITATION STYLE
Guo, X., Zhang, Y., & Xu, J. (2017). Online multi-label passive aggressive active learning algorithm based on binary relevance. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10638 LNCS, pp. 256–266). Springer Verlag. https://doi.org/10.1007/978-3-319-70139-4_26
Mendeley helps you to discover research relevant for your work.