Multilabel classification via calibrated label ranking

By Johannes Fürnkranz, Eyke Hüllermeier, Eneldo Loza Mencía, Klaus Brinker. Machine-mediated learning, 2008

953 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/aaf697ea0489ab9590c55baa470e6510389abe37

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Citations in the corpus

Multi-label Learning with Missing Labels Using Mixed Dependency Graphs (2018, methodology): “Note that the semantic hierarchical constraint used in our model is similar to the ranking constraint (Fürnkranz et al. 2008; Bucak et al. 2011) that is widely used in multi-label ranking models, but there are signiﬁcant differences.”
Convex Two-Layer Modeling (2013, methodology): “Despite their simplicity, large-margin multi-label losses have proved to be highly successful in practice [30, 31].”
Addressing imbalance in multilabel classification: Measures and random resampling algorithms (2015, methodology): “Regarding the MLC algorithms, the following were chosen: CLR [37], RAkEL [34], IBLR [38], and HOMER [35].”
Large-Scale Multi-label Text Classification - Revisiting Neural Networks (2013, background): “In the literature, one can find a number of multi-label classification approaches for a variety of tasks in different domains such as bioinformatics [1], music [33], and text [8].”
Classifier chains for multi-label classification (2009, background): “CLR (Fürnkranz et al. 2008) is a well-known example (many other pairwise methods are specific to the label ranking problem, which is beyond the scope of this paper).”
Scalable and efficient multi-label classification for evolving data streams (2012, methodology): “Gama et al. (2009) propose a forgetting mechanism for estimating holdout accuracy using prequential accuracy: either a sliding window of size w with the most recent observations can be used, or fading factors that weigh observations using a decay factor α.”
Maximizing Subset Accuracy with Recurrent Neural Networks in Multi-label Classification (2017, unknown): “Whereas this is typically solved using a meta learner that predicts a threshold in the ranking of labels, EncDec follows a similar approach as [7] and directly predicts a virtual label that indicates the end of the sequence.”
On label dependence and loss minimization in multi-label classification (2012, methodology): “Ranking by pairwise comparison (Hüllermeier et al. 2008; Fürnkranz et al. 2008) is a reduction method that transform the MLC problem to a quadratic number of binary problems, one for each pair of labels.”
Lift: Multi-Label Learning with Label-Specific Features (2011, methodology): “In this section, the generality of label-specific features is studied by incorporating them into two state-of-the-art multi-label learning algorithms CLR [14] and ECC [38].”
Multilabel Classification with Principal Label Space Transformation (2012, background): “Typical members of problem transformation include label power set, binary relevance, and label ranking (Furnkranz, Hullermeier, Lozamencia, & Brinker, 2008 ; Elisseeff & Weston, 2002 ).”
Label Distribution Learning (2013, background): “In detail, for a problem with c different labels, there are c possible outputs for a single-label learner, and 2 c − 1 possible outputs for a multi-label learner.”
Probabilistic Multi-Label Classification with Sparse Feature Learning (2013, unknown): “A significant number of multi-label learning approaches have been proposed to exploit label dependencies in classification model formulation, including ranking based methods [Elisseeff and Weston, 2002; Schapire and Singer, 2000; Shalev-Shwartz and Singer, 2006; Fuernkranz et al., 2008], pairwise la”
Block-Row Sparse Multiview Multilabel Learning for Image Classification (2016, methodology): “The popular learning techniques include lazy learning [43], kernel techniques [44], information-theoretic techniques [42], etc.”
Multi-label learning by exploiting label dependency (2010, background): “• Second-order approaches: The task of multi-label learning is tackled by considering the pairwise relations between labels, such as the ranking between the proper label and the improper label of an example [7, 8, 19, 28], or the interaction between any pair of labels [9, 16, 24, 30].”
Attend and Imagine: Multi-Label Image Classification With Visual Attention and Recurrent Neural Networks (2019, methodology): “The common problem transformation methods, which transform multi-label problem into multiple single-label problems, contain Binary Relevance (BR) [19], Classifier Chains (CC) [20], [21], Calibrated Label Ranking [22], Label Powerset (LP) [23], [24] and their variants.”