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Peter Reutemann

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2 papers

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JMLR Journal 2016 Journal Article

MEKA: A Multi-label/Multi-target Extension to WEKA

Jesse Read
Peter Reutemann
Bernhard Pfahringer
Geoff Holmes

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. [abs] [ pdf ][ bib ] [ code ] [ webpage ] &copy JMLR 2016. ( edit, beta )

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JMLR Journal 2010 Journal Article

WEKA-Experiences with a Java Open-Source Project

Remco R. Bouckaert
Eibe Frank
Mark A. Hall
Geoffrey Holmes
Bernhard Pfahringer
Peter Reutemann
Ian H. Witten

WEKA is a popular machine learning workbench with a development life of nearly two decades. This article provides an overview of the factors that we believe to be important to its success. Rather than focussing on the software's functionality, we review aspects of project management and historical development decisions that likely had an impact on the uptake of the project. [abs] [ pdf ][ bib ] &copy JMLR 2010. ( edit, beta )

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