A systematic analysis of performance measures for classification tasks
Information Processing and Management: an International Journal
Multi-label Classification with Gene Expression Programming
HAIS '09 Proceedings of the 4th International Conference on Hybrid Artificial Intelligence Systems
Particle swarm optimization for multi-label classification
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
Journal of Artificial Intelligence Research
A study on multi-label classification
ICDM'13 Proceedings of the 13th international conference on Advances in Data Mining: applications and theoretical aspects
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In classic pattern recognition problems, classes are mutually exclusive by definition. However, in many applications, it is quite natural that some instances belong to multiple classes at the same time. In other words, these applications are multi-labeled, classes are overlapped by definition and each instance may be associated to multiple classes. In this paper, we present a comparative study on various multi-label approaches using both gene and scene data sets. We expect our research efforts provide useful insights on the relationships among various classifiers as well as various evaluation measures and shed lights on future research. Although there is no clear winner across various performance measures, SVM Binary and Multi-label ADTree perform better than the others on most counts. We then propose a meta-learning approach by combining SVM binary and ADTree. Our experiments demonstrate that the combined method can take the advantages of the single approaches.