Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence An Ensemble of Bayesian Networks for Multilabel Classification ∗

Citations in the corpus

• Elucidating image-to-set prediction: An analysis of models, losses and datasets (2019, background): “Multi-label classification has been a long lasting problem in computer vision [72, 21, 73, 67] tackled by a wide variety of methods, such as, decomposing the problem into independent single-label classification problems [44, 72], exploiting label co-occurrences [1, 56, 34], introducing priors such a”
• Improving Pairwise Ranking for Multi-label Image Classification (2017, background): “[1] extended single-label naive Bayes classifier to the multi-label case, proposing an ensemble of Bayesian networks in treestructured label space.”
• CU-Net: Component Unmixing Network for Textile Fiber Identification (2019, methodology): “2015), label dependency (Shu et al. 2015; Alessandro et al. 2013; Liu and Tsang 2015; Verma and Jawahar 2016; Zhang and Zhou 2007; Zhou et al. 2012; Wu et al. 2018), and label noise (Wu et al.”
• Binary relevance for multi-label learning: an overview (2018, methodology): “First, a tree-based Bayesian network can be utilized as a simplified DAG structure where second-order label correlations are considered by pruning each class label with (up to) one parent [26,27].”