MEKA: A Multi-label Extension to WEKA

The MEKA project provides an open source implementation of methods for multi-label learning and evaluation. In multi-label classification, we want to predict multiple output variables for each input instance. This different from the'standard' case (binary, or multi-class classification) which involves only a single target variable. MEKA is based on the WEKA Machine Learning Toolkit; it includes dozens of multi-label methods from the scientific literature, as well as a wrapper to the related MULAN framework. NEW RELEASE April 12, 2017: Meka 1.9.1 is released.