Demo Data from EDBT 2026 / reviewers in the wild / expert
Geoff Crew
dblp:05/5092
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2004
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1
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.
| Artificial intelligence
1 paper |
Kernel, tree and ensemble methods · 67% Optimization for machine learning · 33% |
Topics — the 2 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Machine learning › Kernel, tree and ensemble methods
ensemble learning |
0.0 | 1 | 2004 | Ensemble selection from libraries of models · ICML 2004 |
Machine learning › Kernel, tree and ensemble methods › ensemble learning
ensemble selection |
0.0 | 1 | 2004 | Ensemble selection from libraries of models · ICML 2004 |
Methods — techniques the papers use, named apart from their topics
forward stepwise selection · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2004 | Ensemble selection from libraries of modelsabstractWe present a method for constructing ensembles from libraries of thousands of models. Model libraries are generated using different learning algorithms and parameter settings. Forward stepwise selection is used to add to the ensemble the models that maximize its performance. Ensemble selection allows ensembles to be optimized to performance metric such as accuracy, cross entropy, mean precision, or ROC Area. Experiments with seven test problems and ten metrics demonstrate the benefit of ensemble selection. Rich Caruana, Alexandru Niculescu-Mizil, Geoff Crew, Alex Ksikes |
ICML | 3 |