An evaluation of phrasal and clustered representations on a text categorization task
SIGIR '92 Proceedings of the 15th annual international ACM SIGIR conference on Research and development in information retrieval
The nature of statistical learning theory
The nature of statistical learning theory
Context-sensitive learning methods for text categorization
SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval
A re-examination of text categorization methods
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
BoosTexter: A Boosting-based Systemfor Text Categorization
Machine Learning - Special issue on information retrieval
A study of thresholding strategies for text categorization
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
Introduction to Modern Information Retrieval
Introduction to Modern 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
Constraint Classification: A New Approach to Multiclass Classification
ALT '02 Proceedings of the 13th International Conference on Algorithmic Learning Theory
A family of additive online algorithms for category ranking
The Journal of Machine Learning Research
RCV1: A New Benchmark Collection for Text Categorization Research
The Journal of Machine Learning Research
ACM SIGIR Forum
Multilabel Neural Networks with Applications to Functional Genomics and Text Categorization
IEEE Transactions on Knowledge and Data Engineering
LIBLINEAR: A Library for Large Linear Classification
The Journal of Machine Learning Research
Large scale multi-label classification via metalabeler
Proceedings of the 18th international conference on World wide web
Learning multi-label alternating decision trees from texts and data
MLDM'03 Proceedings of the 3rd international conference on Machine learning and data mining in pattern recognition
Selection strategies for multi-label text categorization
FinTAL'06 Proceedings of the 5th international conference on Advances in Natural Language Processing
Evaluation of two systems on multi-class multi-label document classification
ISMIS'05 Proceedings of the 15th international conference on Foundations of Intelligent Systems
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The multi-label classification is a frequent task in machine learning notably in text categorization. When binary classifiers are not suited, an alternative consists in using a multiclass classifier that provides for each document a score per category and then in applying a thresholding strategy in order to select the set of categories which must be assigned to the document. The common thresholding strategies, such as RCut, PCut and SCut methods, need a training step to determine the value of the threshold. To overcome this limit, we propose a new strategy, called MCut which automatically estimates a value for the threshold. This method does not have to be trained and does not need any parametrization. Experiments performed on two textual corpora, XML Mining 2009 and RCV1 collections, show that the MCut strategy results are on par with the state of the art but MCut is easy to implement and parameter free.