Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
ML-KNN: A lazy learning approach to multi-label learning
Pattern Recognition
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Trust Region Newton Method for Logistic Regression
The Journal of Machine Learning Research
Learning to Predict One or More Ranks in Ordinal Regression Tasks
ECML PKDD '08 Proceedings of the 2008 European Conference on Machine Learning and Knowledge Discovery in Databases - Part I
LIBLINEAR: A Library for Large Linear Classification
The Journal of Machine Learning Research
Multi-label Classification Using Ensembles of Pruned Sets
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
M3MIML: A Maximum Margin Method for Multi-instance Multi-label Learning
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Large scale multi-label classification via metalabeler
Proceedings of the 18th international conference on World wide web
Classifier Chains for Multi-label Classification
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part II
Learning Nondeterministic Classifiers
The Journal of Machine Learning Research
Obtaining Bipartitions from Score Vectors for Multi-Label Classification
ICTAI '10 Proceedings of the 2010 22nd IEEE International Conference on Tools with Artificial Intelligence - Volume 01
Random k-Labelsets for Multilabel Classification
IEEE Transactions on Knowledge and Data Engineering
MULAN: A Java Library for Multi-Label Learning
The Journal of Machine Learning Research
Fast multi-label core vector machine
Pattern Recognition
Environmental Modelling & Software
Multi-label classification with a reject option
Pattern Recognition
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In multilabel classification tasks the aim is to find hypotheses able to predict, for each instance, a set of classes or labels rather than a single one. Some state-of-the-art multilabel learners use a thresholding strategy, which consists in computing a score for each label and then predicting the set of labels whose score is higher than a given threshold. When this score is the estimated posterior probability, the selected threshold is typically 0.5. In this paper we introduce a family of thresholding strategies which take into account the posterior probability of all possible labels to determine a different threshold for each instance. Thus, we exploit some kind of interdependence among labels to compute this threshold, which is optimal regarding a given expected loss function. We found experimentally that these strategies outperform other thresholding options for multilabel classification. They provide an efficient method to implement a learner which considers the interdependence among labels in the sense that the overall performance of the prediction of a set of labels prevails over that of each single label.