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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
SDM
2007
SIAM
130views Data Mining» more  SDM 2007»
13 years 6 months ago
Maximizing the Area under the ROC Curve with Decision Lists and Rule Sets
Decision lists (or ordered rule sets) have two attractive properties compared to unordered rule sets: they require a simpler classification procedure and they allow for a more co...
Henrik Boström
ICDM
2006
IEEE
182views Data Mining» more  ICDM 2006»
13 years 10 months ago
Active Learning to Maximize Area Under the ROC Curve
In active learning, a machine learning algorithm is given an unlabeled set of examples U, and is allowed to request labels for a relatively small subset of U to use for training. ...
Matt Culver, Kun Deng, Stephen D. Scott
DATAMINE
2008
112views more  DATAMINE 2008»
13 years 4 months ago
PRIE: a system for generating rulelists to maximize ROC performance
Rules are commonly used for classification because they are modular, intelligible and easy to learn. Existing work in classification rule learning assumes the goal is to produce ca...
Tom Fawcett
IBPRIA
2009
Springer
13 years 9 months ago
Score Fusion by Maximizing the Area under the ROC Curve
Information fusion is currently a very active research topic aimed at improving the performance of biometric systems. This paper proposes a novel method for optimizing the paramete...
Mauricio Villegas, Roberto Paredes