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Multi-label classification is a challenging research problem due to the fact that each example may belong to a varying number of classes. This problem can be further aggravated by high dimensionality and complex correlation among labels. In this paper, a discriminant approach to multi-label classification is proposed using the concept of stacking and spectral regression based kernel discriminant analysis...
Multilabel classification is a challenging research problem in which each instance may belong to more than one class. Recently, a considerable amount of research has been concerned with the development of “good” multi-label learning methods. Despite the extensive research effort, many scientific challenges posed by e.g. highly imbalanced training sets and correlation among labels remain to be addressed...
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