Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
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
Hierarchical multilabel protein function prediction using local neural networks
BSB'11 Proceedings of the 6th Brazilian conference on Advances in bioinformatics and computational biology
An efficient multi-label support vector machine with a zero label
Expert Systems with Applications: An International Journal
Multi-Label Classification Method for Multimedia Tagging
International Journal of Multimedia Data Engineering & Management
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This paper considers the multilabel classification problem, which is a generalization of traditional two-class or multi-class classification problem. In multilabel classification a set of labels (categories) is given and each training instance is associated with a subset of this label-set. The task is to output the appropriate subset of labels (generally of unknown size) for a given, unknown testing instance. Some improvements to the existing neural network multilabel classification algorithm, named BP-MLL, are proposed here. The modifications concern the form of the global error function used in BP-MLL. The modified classification system is tested in the domain of functional genomics, on the yeast genome data set. Experimental results show that proposed modifications visibly improve the performance of the neural network based multilabel classifier. The results are statistically significant.