Demo Data from VLDB 2026 / reviewers in the wild / expert
Peter Reutemann
dblp:11/1891
· DBLP profile ↗
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
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Data mining › predictive modeling › classification
multi-label classification |
0.2 | 1 | 2016 | MEKA: A Multi-label/Multi-target Extension to WEKA · J. Mach. Learn. Res. 2016 |
Empirical software engineering › open source software
open-source projects |
0.1 | 1 | 2010 | WEKA - Experiences with a Java Open-Source Project · J. Mach. Learn. Res. 2010 |
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2016 | MEKA: A Multi-label/Multi-target Extension to WEKAabstractMulti-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 |
PAKDD | 3 |
| 2006 | Using Weighted Nearest Neighbor to Benefit from Unlabeled Data
Kurt Driessens, Peter Reutemann, Bernhard Pfahringer, Claire Leschi |
PAKDD | 2 |