MEKA: A Multi-label/Multi-target Extension to WEKA
Jesse Read, Peter Reutemann, Bernhard Pfahringer, Geoff Holmes; 17(21):1−5, 2016.
Abstract
Multi-label classification has rapidly attracted interest in the machine learning literature, and there are now a large number and considerable variety of methods for this type of learning. We present MEKA: an open-source Java framework based on the well-known WEKA library. MEKA provides interfaces to facilitate practical application, and a wealth of multi-label classifiers, evaluation metrics, and tools for multi-label experiments and development. It supports multi-label and multi-target data, including in incremental and semi- supervised contexts.
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