Term-weighting approaches in automatic text retrieval
Information Processing and Management: an International Journal
Foundations of statistical natural language processing
Foundations of statistical natural language processing
BoosTexter: A Boosting-based Systemfor Text Categorization
Machine Learning - Special issue on information retrieval
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms
Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms
Journal of Intelligent Information Systems
Multilabel Neural Networks with Applications to Functional Genomics and Text Categorization
IEEE Transactions on Knowledge and Data Engineering
Supervised and Traditional Term Weighting Methods for Automatic Text Categorization
IEEE Transactions on Pattern Analysis and Machine Intelligence
Proposing a new term weighting scheme for text categorization
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
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Automatic text classification is the task of assigning unseen documents to a predefined set of classes. Text representation for classification purposes has been traditionally approached using a vector space model due to its simplicity and good performance. On the other hand, multi-label automatic text classification has been typically addressed either by transforming the problem under study to apply binary techniques or by adapting binary algorithms to work with multiple labels. In this paper we present two new representations for text documents based on label-dependent term-weighting for multi-label classification. We focus on modifying the input. Performance was tested with a wellknown dataset and compared to alternative techniques. Experimental results based on Hamming loss analysis show an improvement against alternative approaches.