Sciweavers

ROCAI
2004
Springer
13 years 10 months ago
ROC Optimisation of Safety Related Systems
Abstract. Many safety related and critical systems warn of potentially dangerous events; for example the Short Term Conflict Alert (STCA) system warns of airspace infractions betw...
Jonathan E. Fieldsend, Richard M. Everson
ROCAI
2004
Springer
13 years 10 months ago
Learning Mixtures of Localized Rules by Maximizing the Area Under the ROC Curve
We introduce a model class for statistical learning which is based on mixtures of propositional rules. In our mixture model, the weight of a rule is not uniform over the entire ins...
Tobias Sing, Niko Beerenwinkel, Thomas Lengauer
ROCAI
2004
Springer
13 years 10 months ago
Learning Interestingness Measures in Terminology Extraction. A ROC-based approach
Abstract. In the field of Text Mining, a key phase in data preparation is concerned with the extraction of terms, i.e. collocation of words attached to specific concepts (e.g. Ph...
Mathieu Roche, Jérôme Azé, Yve...
ROCAI
2004
Springer
13 years 10 months ago
Optimizing Area Under Roc Curve with SVMs
For many years now, there is a growing interest around ROC curve for characterizing machine learning performances. This is particularly due to the fact that in real-world problems ...
Alain Rakotomamonjy
ROCAI
2004
Springer
13 years 10 months ago
ROC Analysis of Example Weighting in Subgroup Discovery
This paper presents two new ways of example weighting for subgroup discovery. The proposed example weighting schemes are applicable to any subgroup discovery algorithm that uses th...
Branko Kavsek, Nada Lavrac, Ljupco Todorovski
ROCAI
2004
Springer
13 years 10 months ago
Cautious Classifiers
César Ferri, José Hernández-O...
ROCAI
2004
Springer
13 years 10 months ago
What ROC Curves Can't Do (and Cost Curves Can)
Abstract. This paper shows that ROC curves, as a method of visualizing classifier performance, are inadequate for the needs of Artificial Intelligence researchers in several sign...
Chris Drummond, Robert C. Holte
ROCAI
2004
Springer
13 years 10 months ago
An Empirical Evaluation of Supervised Learning for ROC Area
We present an empirical comparison of the AUC performance of seven supervised learning methods: SVMs, neural nets, decision trees, k-nearest neighbor, bagged trees, boosted trees,...
Rich Caruana, Alexandru Niculescu-Mizil