Incremental Algorithms for Hierarchical Classification

By Nicolò Cesa-Bianchi, Claudio Gentile, Andrea Tironi, Luca Zaniboni. Neural Information Processing Systems, 2004

296 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/4bcf06dc6a371fea69d846c7ccfde4e7ab216408

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Citations in the corpus

Hierarchical Classification via Orthogonal Transfer (2011, methodology): “Another popular method for hierarchical classiﬁcation is to use tree-induced loss functions (Cai & Hofmann, 2004; Dekel et al., 2004; Cesa-Bianchi et al., 2006).”
Kernel-Based Learning of Hierarchical Multilabel Classification Models (2006, methodology): “We compare the new algorithm in Section 5 to flat and hierarchical SVM learning approaches and the hierarchical regularized least squares algorithm recently proposed by Cesa-Bianchi et al. (2004).”
Hedging your bets: Optimizing accuracy-specificity trade-offs in large scale visual recognition (2012, background): “Our problem is related to cost-sensitive classification and hierarchical classification [33, 22, 7, 9, 4, 2, 14, 1, 35, 29, 20, 21, 16, 23, 34, 5, 18].”
Large Scale Visual Recognition (2012, background): “Our problem is related to cost-sensitive classification and hierarchical classification [126, 127, 52, 53, 128, 26, 27, 29, 24, 31, 23, 28, 25, 22, 129, 130, 131].”
FastXML: a fast, accurate and stable tree-classifier for extreme multi-label learning (2014, background): “Finally, care should be taken to note that our objective of learning a multi-label hierarchy is very different from the objective of learning a multi-class hierarchy [5, 11, 13, 16] or exploiting a pre-existing multi-label hierarchy [7, 9, 27].”
Multi-Label Prediction via Compressed Sensing (2009, background): “class hierarchy), efficient learning and prediction methods are often possible [5, 6, 7, 8, 9].”
A Literature Survey on Algorithms for Multi-label Learning (2010, background): “HIerarchical loss extends hamming loss to account for any existing underlying hierarchical strucuture of the labels [7].”
Mining Multi-label Data (2010, methodology): “The hierarchical loss [54] is a modified version of the Hamming loss that takes into account an existing hierarchical structure of the labels.”
Decision trees for hierarchical multi-label classification (2008, background): “stage, so that a classifier constructed for a class c can only predict positive if the classifier for the parent class of c has predicted positive (Barutcuoglu et al. 2006; Cesa-Bianchi et al. 2006).”
Hierarchical Multi-Label Classification Networks (2018, unknown): “Typical HMC problems are text classification (Rousu et al., 2006; Cesa-Bianchi et al., 2006; Mayne & Perry, 2009; Lewis et al., 2004; Klimt & Yang, 2004), image annotation (Dimitrovski et al., 2011), and bioinformatics tasks such as protein function prediction (Otero et al., 2010; Valentini, 2011;…”
Efficient max-margin multi-label classification with applications to zero-shot learning (2012, methodology): “Methods such as Cai and Hofmann (2007), Cesa-Bianchi et al. (2006) optimise a hierarchical loss over the tree structure but do not incorporate pairwise, or higher order, label interaction terms.”
Multi-label learning with millions of labels: recommending advertiser bid phrases for web pages (2013, background): “Hierarchies can make it easier to learn from a large number of labels [5, 12, 33].”
Recursive regularization for large-scale classification with hierarchical and graphical dependencies (2013, methodology): “The more principled methods include the large-margin models by [31],[6],[26],[9],[7] where the discriminant functions take the contributions from all nodes along the path to the root, and the model parameters are jointly learned to minimize a global loss over the hierarchy.”