Hello, My name is Alain Pena, (now previously) student in computer engineering at University of Liège. For my master thesis, I had to implement some methods for multilabel classification, those methods being RAKEL [1] and (Ensemble) Classifier Chain [2], as well as some variants of this latter (order of the chain or length of its links for example).
They are currently lazy (I had a problem with memory while doing my thesis, so I had to implement them lazily, throwing each estimator away) as well as single threaded. They are tested for multilabel only with a test coverage of about 80%. Before eventually upgrading them to make them more robust and versatile, I wondered if scikit-learn would have any interest in those methods. Best regards. Alain Pena. [1] Tsoumakas, G. and Vlahavas, I. (2007). Random k-labelsets: An ensemble method for multilabel classification. In Machine learning: ECML 2007, pages 406–417. Springer. [2] Read, J., Pfahringer, B., Holmes, G., and Frank, E. (2011). Classifier chains for multi-label classification. Machine learning, 85(3):333–359. ------------------------------------------------------------------------------ Don't Limit Your Business. Reach for the Cloud. GigeNET's Cloud Solutions provide you with the tools and support that you need to offload your IT needs and focus on growing your business. Configured For All Businesses. Start Your Cloud Today. https://www.gigenetcloud.com/ _______________________________________________ Scikit-learn-general mailing list Scikit-learn-general@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
