Classifier chains for multi-label classification

Jesse Read; Bernhard Pfahringer; Geoffrey Holmes; Eibe Frank

doi:https://doi.org/10.1007/s10994-011-5256-5

Classifier chains for multi-label classification

2011 · Machine Learning · 2,307 citations · 2 from inside this corpus

Jesse Read, Bernhard Pfahringer, Geoffrey Holmes, Eibe Frank

The widely known binary relevance method for multi-label classification, which considers each label as an independent binary problem, has often been overlooked in the literature due to the perceived inadequacy of not directly modelling label correlations. Most current methods invest considerable complexity to model interdependencies between labels. This paper shows that binary relevance-based methods have much to offer, and that high predictive performance can be obtained without impeding scalability to large datasets. We exemplify this with a novel classifier chains method that can model label correlations while maintaining acceptable computational complexity. We extend this approach further in an ensemble framework. An extensive empirical evaluation covers a broad range of multi-label datasets with a variety of evaluation metrics. The results illustrate the competitiveness of the chaining method against related and state-of-the-art methods, both in terms of predictive performance and time complexity.

13 of 13 neighbouring works in this corpus. Blue is what this paper cites; orange is what cites it, and a dashed line is one neighbour citing another. Only the largest labels are drawn — every node carries its full title on hover.

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Citation edge evidence

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A Review on Multi-Label Learning Algorithms→Classifier chains for multi-label classificationLegacy edge — source detail unavailable
Ensemble deep learning: A review→Classifier chains for multi-label classificationLegacy edge — source detail unavailable
Classifier chains for multi-label classification→Bagging PredictorsLegacy edge — source detail unavailable
Classifier chains for multi-label classification→Induction of Decision TreesLegacy edge — source detail unavailable
Classifier chains for multi-label classification→Statistical Comparisons of Classifiers over Multiple Data SetsLegacy edge — source detail unavailable
Classifier chains for multi-label classification→Advances in Neural Information Processing Systems 14Legacy edge — source detail unavailable
Classifier chains for multi-label classification→Probabilistic Outputs for Support vector Machines and Comparisons to Regularized Likeliho…Legacy edge — source detail unavailable
Classifier chains for multi-label classification→ML-KNN: A lazy learning approach to multi-label learningLegacy edge — source detail unavailable
Classifier chains for multi-label classification→A Short Introduction to BoostingLegacy edge — source detail unavailable
Classifier chains for multi-label classification→Improved boosting algorithms using confidence-rated predictionsLegacy edge — source detail unavailable
Classifier chains for multi-label classification→Multi-Label ClassificationLegacy edge — source detail unavailable
Classifier chains for multi-label classification→BoosTexter: A Boosting-based System for Text CategorizationLegacy edge — source detail unavailable
Classifier chains for multi-label classification→Improved Boosting Algorithms Using Confidence-rated PredictionsLegacy edge — source detail unavailable
ML-KNN: A lazy learning approach to multi-label learning→Improved boosting algorithms using confidence-rated predictionsLegacy edge — source detail unavailable
ML-KNN: A lazy learning approach to multi-label learning→BoosTexter: A Boosting-based System for Text CategorizationLegacy edge — source detail unavailable
ML-KNN: A lazy learning approach to multi-label learning→Advances in Neural Information Processing Systems 14Legacy edge — source detail unavailable
BoosTexter: A Boosting-based System for Text Categorization→Improved boosting algorithms using confidence-rated predictionsLegacy edge — source detail unavailable
A Review on Multi-Label Learning Algorithms→ML-KNN: A lazy learning approach to multi-label learningLegacy edge — source detail unavailable
A Review on Multi-Label Learning Algorithms→BoosTexter: A Boosting-based System for Text CategorizationLegacy edge — source detail unavailable
A Review on Multi-Label Learning Algorithms→Multi-Label ClassificationLegacy edge — source detail unavailable
A Short Introduction to Boosting→Improved boosting algorithms using confidence-rated predictionsLegacy edge — source detail unavailable
A Short Introduction to Boosting→BoosTexter: A Boosting-based System for Text CategorizationLegacy edge — source detail unavailable
Multi-Label Classification→BoosTexter: A Boosting-based System for Text CategorizationLegacy edge — source detail unavailable
Ensemble deep learning: A review→Multi-Label ClassificationLegacy edge — source detail unavailable
Ensemble deep learning: A review→Bagging PredictorsLegacy edge — source detail unavailable

What this paper cites, inside the corpus

Paper	Year	Cited
Bagging Predictors	1996	17,191
Induction of Decision Trees	1986	15,020
Statistical Comparisons of Classifiers over Multiple Data Sets	2006	11,215
Advances in Neural Information Processing Systems 14	2002	8,961
Probabilistic Outputs for Support vector Machines and Comparisons to Regularized Likeliho…	1999	4,873
ML-KNN: A lazy learning approach to multi-label learning	2007	3,587
A Short Introduction to Boosting	1999	2,952
Improved boosting algorithms using confidence-rated predictions	1998	2,573
Multi-Label Classification	2007	2,518
BoosTexter: A Boosting-based System for Text Categorization	2000	2,200
Improved Boosting Algorithms Using Confidence-rated Predictions	1999	1,966

What cites it, inside the corpus

Paper	Year	Cited
A Review on Multi-Label Learning Algorithms	2013	3,375
Ensemble deep learning: A review	2022	2,136

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