Scikit-multilearn-ng

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- *kNN classifiers support sparse matrices properly
- support for the new model_selection API from scikit-learn
- extended graph-based label space clusteres to allow taking probability of a label occuring alone into consideration
- compatible with newest graphtool
- support the case when meka decides that an observation doesn't have any labels assigned
- HARAM classifier provided by Fernando Benitez from University of Konstanz
- predict_proba added to problem transformation classifiers
- ported to python 3

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- support for new multi-label classification methods:
- classsifier chains (CC)
- multi-label kNN methods: BRkNN and MLkNN
- all classifiers use sparse matrices internally
- a general network for clustering label space with a flat classifier
- the classifiers work with scikit pipelines / CVs

- interface to meka 1.9, meka can work as a scikit-ml classifier
- loading arff files to sparse matrices by default

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- support for new multi-label classification methods:
- classsifier chains (CC)
- multi-label kNN methods: BRkNN and MLkNN
- all classifiers use sparse matrices internally
- a general network for clustering label space with a flat classifier
- the classifiers work with scikit pipelines / CVs

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- initial release
- support for initial set of multi-label classification methods:
- binary relevance, label powerset
- RAkEL both distinct and overlapping
- label cooccurence based distinct partitioning classifiers
- interface to meka 1.7
- ARFF to numpy.array convertion classes and data set manipulation