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Issue No.10 - October (2006 vol.18)
pp: 1338-1351
ABSTRACT
In multilabel learning, each instance in the training set is associated with a set of labels and the task is to output a label set whose size is unknown a priori for each unseen instance. In this paper, this problem is addressed in the way that a neural network algorithm named BP-MLL, i.e., Backpropagation for Multilabel Learning, is proposed. It is derived from the popular Backpropogation algorithm through employing a novel error function capturing the characteristics of multilabel learning, i.e., the labels belonging to an instance should be ranked higher than those not belonging to that instance. Applications to two real-world multilabel learning problems, i.e., functional genomics and text categorization, show that the performance of BP-MLL is superior to that of some well-established multilabel learning algorithms.
INDEX TERMS
Machine learning, data mining, multilabel learning, neural networks, backpropagation, functional genomics, text categorization.
CITATION
Min-Ling Zhang, Zhi-Hua Zhou, "Multilabel Neural Networks with Applications to Functional Genomics and Text Categorization", IEEE Transactions on Knowledge & Data Engineering, vol.18, no. 10, pp. 1338-1351, October 2006, doi:10.1109/TKDE.2006.162
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