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Peter Reutemann

dblp:11/1891 · DBLP profile ↗
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5ranked-venue papers
1as first author
0since 2021 · last 2016
0000-0002-1226-0948ORCID · corroborated

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 3 · 1 first-authorDatabases, data management, data science and information retrieval · 3 · 1 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Databases, data mining, and information retrieval
1 paper
Data mining · 50% Machine learning and data management · 50%
Software engineering, system software, and programming languages
1 paper
Empirical software engineering · 100%

Topics — the 2 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Data mining › predictive modeling › classification
multi-label classification
0.212016
MEKA: A Multi-label/Multi-target Extension to WEKA · J. Mach. Learn. Res. 2016
Empirical software engineering › open source software
open-source projects
0.112010
WEKA - Experiences with a Java Open-Source Project · J. Mach. Learn. Res. 2010
YearPublicationVenuePosition
2016 MEKA: A Multi-label/Multi-target Extension to WEKA
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.
Jesse Read, Peter Reutemann, Bernhard Pfahringer, Geoff Holmes 0001
J. Mach. Learn. Res.2
2012 Scientific Workflow Management with ADAMS
Peter Reutemann, Joaquin Vanschoren
ECML/PKDD (2)1
2010 WEKA - Experiences with a Java Open-Source Project
Remco R. Bouckaert, Eibe Frank, Mark A. Hall, Geoff Holmes 0001, Bernhard Pfahringer, Peter Reutemann, Ian H. Witten
J. Mach. Learn. Res.6
2007 Scaling Up Semi-supervised Learning: An Efficient and Effective LLGC Variant
Bernhard Pfahringer, Claire Leschi, Peter Reutemann
PAKDD3
2006 Using Weighted Nearest Neighbor to Benefit from Unlabeled Data
Kurt Driessens, Peter Reutemann, Bernhard Pfahringer, Claire Leschi
PAKDD2