Discriminative Methods for Multi-labeled Classification

By Shantanu Godbole, Sunita Sarawagi. Pacific-Asia Conference on Knowledge Discovery and Data Mining, 2004

830 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems

View Paper: https://www.semanticscholar.org/paper/b1cec9751c8b5c90ae53a97c2b436dbe6e43153e

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Citations in the corpus

Structured Prediction Energy Networks (2015, background): “Modeling improvements use max-margin or ranking losses that directly address the multi-label structure (Elisseeff & Weston, 2001; Godbole & Sarawagi, 2004; Bucak et al., 2009).”
A Tutorial on Multilabel Learning (2015, methodology): “IR has been computed for each labelset as the quotient between the size of the most frequent labelset and the size of the labelset.”
Learning Spatial Regularization with Image-Level Supervisions for Multi-label Image Classification (2017, background): “Multi-label classification has applications in many areas, such as document topic categorization [29, 10], music annotation and retrieval [35], scene recognition [4], and gene functional analysis [2].”
Scalable and efficient multi-label classification for evolving data streams (2012, methodology): “This method is batch-incremental, using K batches of S examples to train meta-BR (MBR); a well-known multi-label method described in Godbole and Sarawagi (2004), where the outputs of one BR are used in the attribute space of a second BR (a form of stacking, employed to take into account label…”
DEMON: a local-first discovery method for overlapping communities (2012, background): “For alternatives multi-label evaluations, we refer to [11].”
Multi-Label Classification: An Overview (2007, methodology): “Godbole and Sarawagi (2004) present two improvements for the Support Vector Machine (SVM) classifier in conjunction with the PT4 method for multi-label classification.”
Binary relevance for multi-label learning: an overview (2018, methodology): “Algorithm 3 Pseudo-code of stacking aggregation [19] Inputs: D: Multi-label training set {(xi , yi) | 1 i m} (xi ∈ X, yi ∈ {−1,+1}q ,X = Rd ,Y = {λ1, λ2, .”
On the Consistency of Multi-Label Learning (2011, neutral): “…called evaluation criteria ), e.g., ranking loss , hamming loss , one-error , coverage and average precision (Schapire and Singer, 2000; Zhang and Zhou, 2006); accuracy , precision , recall and F 1 (Godbole and Sarawagi, 2004; Qi et al., 2007); subset accuracy (Ghamrawi and McCallum, 2005); etc.”
Addressing imbalance in multilabel classification: Measures and random resampling algorithms (2015, methodology): “Among the transformation methods, the most popular are those based on the binarization of the MLD, such as Binary Relevance (BR) [11] and Ranking by Pairwise Comparison [12], and the LP [13] transformation, which produces a multiclass dataset from an MLD considering each labelset as class.”
An extensive experimental comparison of methods for multi-label learning (2012, methodology): “[21] present algorithms which extend the SVM binary classifiers along two dimensions: training set extension and improvement of margin.”
Thou shalt not hate: Countering Online Hate Speech (2018, methodology): “In this paper, the multi-label classiﬁers are evaluated using ﬁve measures: accuracy, precision, recall, F1-score and hamming loss (Godbole and Sarawagi 2004; Schapire and Singer 2000).”
Multi-label learning by exploiting label dependency (2010, background): “• High-order approaches: The task of multi-label learning is tackled by considering the high-order relations between labels, such as the full-order style of imposing all other labels’ influences on each label in an indirect manner [3, 10, 11, 25], or the random style of combining an ensemble of clas”
Explainable and Interpretable Models in Computer Vision and Machine Learning (2017, background): “2014), only the predictions in a pure stacking manner (Godbole and Sarawagi 2004), or Gibbs sampling instead of the first level of classifiers (Guo and Gu 2011).”
Probabilistic Multi-Label Classification with Sparse Feature Learning (2013, neutral): “…literature, many approaches have been proposed to address multi-label learning by either exploiting label dependencies [Elisseeff and Weston, 2002; Godbole and Sarawagi, 2004; Guo and Gu, 2011; Schapire and Singer, 2000; Petterson and Caetano, 2011], or capturing the common prediction structures…”
Learning fine-grained features via a CNN Tree for Large-scale Classification (2015, background): “Some researchers have already proposed to explore the relationships among classes based on the confusion matrix [44], [47], [49], [50].”