Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
BoosTexter: A Boosting-based Systemfor Text Categorization
Machine Learning - Special issue on information retrieval
Knowledge Discovery in Multi-label Phenotype Data
PKDD '01 Proceedings of the 5th European Conference on Principles of Data Mining and Knowledge Discovery
MMAC: A New Multi-Class, Multi-Label Associative Classification Approach
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Collective multi-label classification
Proceedings of the 14th ACM international conference on Information and knowledge management
ML-KNN: A lazy learning approach to multi-label learning
Pattern Recognition
Label ranking by learning pairwise preferences
Artificial Intelligence
Multilabel classification via calibrated label ranking
Machine Learning
Multi-label Classification Using Ensembles of Pruned Sets
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Classifier Chains for Multi-label Classification
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part II
Multi-label learning by exploiting label dependency
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Multilabel dimensionality reduction via dependence maximization
ACM Transactions on Knowledge Discovery from Data (TKDD)
Multi-dimensional classification with Bayesian networks
International Journal of Approximate Reasoning
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
Two stage architecture for multi-label learning
Pattern Recognition
Approximating discrete probability distributions with dependence trees
IEEE Transactions on Information Theory
Multi-label classification using conditional dependency networks
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
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There always exists some kind of label dependency in multi-label data. Learning and utilizing those dependencies could improve the learning performance further. Therefore, an approach for multi-label learning is proposed in this paper, which quantifies the dependencies of pairwise labels firstly, and then builds a tree structure of the labels to describe them. Thus the approach could find out potential strong label dependencies and produce more generalized dependent relationships. The experimental results have validated that compared with other state-of-the-art algorithms, the method is not only a competitive alternative, but also has shown better performance after ensemble learning especially.