I previously published a paper "PRIE: A system for generating rulelists to maximize ROC performance" (Data Mining and Knowledge Discovery, V.17 No.2 / October, 2008). I have just released Pyriel, an open-source implementation of this system. It is available here:
Pyriel is a Python system for learning classification rules from data. Unlike other rule learning systems, it is designed to learn rule lists that maximize the area under the ROC curve (AUC) instead of accuracy. Pyriel is mostly an experimental research tool, but I believe it's robust and fast enough to be used for some industrial data mining.
Pyriel handles numerical and categorical attributes naturally, as well as dates and times. Pyriel can handle set-valued attributes (Cohen, 1996), in which an attribute of an instance may take on a set of discrete values. Pyriel can handle an arbitrary number of classes. It will attempt to optimize the combined AUC for any number of classes simultaneously. The output is a single rulelist and thus is relatively intelligible and modular.