On Fri, Jul 10, 2015 at 3:39 PM, Dale Smith <dsm...@nexidia.com> wrote:
> See
>
>
> http://scikit-learn.org/stable/faq.html#can-i-add-this-new-algorithm-that-i-or-someone-else-just-published
>
They pass the formal tests:
[1] has 361 cites and is 8 years old
[2] has 463 and is 4 years old
It will need somebody who knows about these to assess whether this is a
good contribution.
>
> 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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