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
Classifier Chains for Multi-label Classification
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: 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
Incorporating label dependency into the binary relevance framework for multi-label classification
Expert Systems with Applications: An International Journal
Evaluation of Label Dependency for the Prediction of HLA Genes
Proceedings of the International Conference on Bioinformatics, Computational Biology and Biomedical Informatics
| Hi-index | 0.00 |
In multi-label classification, each example can be associated with multiple labels simultaneously. The task of learning from multilabel 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 multilabel 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. In this paper, we consider a simple approach which can be used to explore labels dependency aiming to accurately predict label combinations. An experimental study using decision trees, a kernel method as well as Naïve Bayes as base-learning techniques shows the potential of the proposed approach to improve the multi-label classification performance.