Improved Boosting Algorithms Using Confidence-rated Predictions
Machine Learning - The Eleventh Annual Conference on computational Learning Theory
A study of the behavior of several methods for balancing machine learning training data
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
Correlated Label Propagation with Application to Multi-label Learning
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Correlative multi-label video annotation
Proceedings of the 15th international conference on Multimedia
Expert Systems with Applications: An International Journal
Extracting shared subspace for multi-label classification
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Hypergraph spectral learning for multi-label classification
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
The multi-label OCS with a genetic algorithm for rule discovery: implementation and first results
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Classifier Chains for Multi-label Classification
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part II
Expert Systems with Applications: An International Journal
A simple approach to incorporate label dependency in multi-label classification
MICAI'10 Proceedings of the 9th Mexican international conference on Artificial intelligence conference on Advances in soft computing: Part II
Decision trees for hierarchical multilabel classification: a case study in functional genomics
PKDD'06 Proceedings of the 10th European conference on Principle and Practice of Knowledge Discovery in Databases
Semantic Home Photo Categorization
IEEE Transactions on Circuits and Systems for Video Technology
SBIA'12 Proceedings of the 21st Brazilian conference on Advances in Artificial Intelligence
A Comparison of Multi-label Feature Selection Methods using the Problem Transformation Approach
Electronic Notes in Theoretical Computer Science (ENTCS)
Random block coordinate descent method for multi-label support vector machine with a zero label
Expert Systems with Applications: An International Journal
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In multi-label classification, examples can be associated with multiple labels simultaneously. The task of learning from multi-label data can be addressed by methods that transform the multi-label classification problem into several single-label classification problems. The binary relevance approach is one of these methods, where the multi-label learning task is decomposed into several independent binary classification problems, one for each label in the set of labels, and the final labels for each example are determined by aggregating the predictions from all binary classifiers. However, this approach fails to consider any dependency among the labels. Aiming to accurately predict label combinations, in this paper we propose a simple approach that enables the binary classifiers to discover existing label dependency by themselves. An experimental study using decision trees, a kernel method as well as Naive Bayes as base-learning techniques shows the potential of the proposed approach to improve the multi-label classification performance.