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.
From the reference paper's first algorithm (Fig. 1) it seems as if in training the data should be augmented with the true label, not the predicted label. However, they then say "the chaining procedure implies that CC cannot be parallelized"; do they mean the fitting or the predicting? Have you referred to the MEKA implementation?