Learning Mixtures of Localized Rules by Maximizing the Area Under the ROC Curve
Tobias Sing, Niko Beerenwinkel, Thomas Lengauer, José Hernández‐Orallo, Cèsar Ferri, Nicolas Lachiche, Peter A. Falch · 2004
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 instance space. Rather, it depends on the instance at hand. This is motivated by applications in molecular biology, where it is frequently observed that the effect of a particular mutational pattern depends on the genetic background in which it occurs. We assume in our model that the effect of a given pattern of mutations will be very similar only among sequences that are also highly similar to each other. On the other hand, a pattern might have very different effects in different genetic backgrounds.