January 3, 2025Journal of Machine Learning Research217 citationsOpen Access

MEKA: A multi-label/multi-target extension to Weka

JRJesse ReadCentre National de la Recherche Scientifique PRPeter ReutemannUniversity of Waikato BPBernhard PfahringerUniversity of Vienna

Key Points

Multi-label classification facilitates handling complex datasets, and MEKA provides a robust framework.
It includes multi-label classifiers which enhance the performance of machine learning tasks.
Open-source software allows for flexible application and adaptation across various scenarios.
Supports both incremental and semi-supervised contexts, showcasing its versatility in machine learning.

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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