See

http://scikit-learn.org/stable/faq.html#can-i-add-this-new-algorithm-that-i-or-someone-else-just-published

You can add your work as a related project.

http://scikit-learn.org/stable/related_projects.html

https://github.com/scikit-learn/scikit-learn/wiki/Third-party-projects-and-code-snippets

http://scikit-learn.org/stable/developers/index.html

But also review
Dale Smith, Ph.D.
Data Scientist
​


d. 404.495.7220 x 4008   f. 404.795.7221
Nexidia Corporate | 3565 Piedmont Road, Building Two, Suite 400 | Atlanta, GA 
30305

    


-----Original Message-----
From: Al [mailto:alain.pen...@gmail.com] 
Sent: Friday, July 10, 2015 9:21 AM
To: scikit-learn-general@lists.sourceforge.net
Subject: [Scikit-learn-general] Estimators of RAKEL and (Ensemble) Classifier 
Chain for multilabel proposal

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.

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