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
Multilabel Neural Networks with Applications to Functional Genomics and Text Categorization
IEEE Transactions on Knowledge and Data Engineering
ML-KNN: A lazy learning approach to multi-label learning
Pattern Recognition
Multiobjective Optimization in Bioinformatics and Computational Biology
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Muiltiobjective optimization using nondominated sorting in genetic algorithms
Evolutionary Computation
Feature selection for multi-label naive Bayes classification
Information Sciences: an International Journal
A Fast Multi-label Classification Algorithm Based on Double Label Support Vector Machine
CIS '09 Proceedings of the 2009 International Conference on Computational Intelligence and Security - Volume 02
Learning multi-label alternating decision trees from texts and data
MLDM'03 Proceedings of the 3rd international conference on Machine learning and data mining in pattern recognition
Improving multilabel classification performance by using ensemble of multi-label classifiers
MCS'10 Proceedings of the 9th international conference on Multiple Classifier Systems
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
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In multi-label classification problems, some samples belong to multiple classes simultaneously and thus the classes are not mutually exclusive. How to characterize this kind of correlations between labels has been a key issue for designing a new multi-label classification approach. In this paper, we define two objective functions, i.e., the number of relevant and irrelevant label pairs which are ranked incorrectly, and the model regularization term, which depict the correlations between labels and the model complexity respectively. Then a new kernel machine for multi-label classification is constructed using two-objective minimization and solved by fast and elitist multi-objective genetic algorithm, i.e., NSGA-II. Experiments on the benchmark data set Yeast illustrate that our multi-label method is a competitive candidate for multilabel classification, compared with several state-of-the-art methods.