Multi label related algorithms

Hi,

My research project mainly focuses on multi-label classification, but I found there is only limited support for multi-label classification in scikit-learn (there are multiclass module, but multi-label has some different perspectives and thus can use some different algorithms).

Therefore, I'd like to help implement some novel algorithms for MLC in sklearn. I plan to

1. Add some multi-label classification datasets to sklearn, e.g. [mulan's mlc datasets](http://mulan.sourceforge.net/datasets-mlc.html). (I have added a dataset "emotions" this time, but I wonder should I create a new "sub module" for these multi label datasets, or should I place them in `base.py`?)
2. Implement some basic MLC algorithms
3. Implement some novel multi-label cost-sensitive classfication algorithms, as people in my lab ([CL Lab](http://main.learner.csie.ntu.edu.tw/doku.php)) have some contributions on this area

What do you think?
Should we merge this PR first, or should we keep it until I finish all my work?

Thanks.
As in `OneVsRestClassifier` and cross validation tools, you should `clone(...)` a constructed estimator rather than accepting a class as a parameter.