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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 backpropagation 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
Neural networks, Genomics, Bioinformatics, Text categorization, Backpropagation algorithms, Learning systems, Layout, Data mining, Government, Biochemistry,text categorization., Machine learning, data mining, multilabel learning, neural networks, backpropagation, functional genomics
CITATION
"Multilabel Neural Networks with Applications to Functional Genomics and Text Categorization", IEEE Transactions on Knowledge & Data Engineering, vol. 18, no. , pp. 1338-1351, October 2006, doi:10.1109/TKDE.2006.162
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