Decision list machines.

Abstract

We learn decision lists over a space of features that are constructed from the data. A practical machine which we call the Decision List Machine comes as a result. We construct the Decision List Machine which uses generalized balls as data-dependent features. We compare practical performance on some data sets with the performance of some other learning algorithms such as the Set Covering Machine and the Support Vector Machine. This performance is evaluated for both symmetric and asymmetric loss coefficients. We also provide a theoretical assessment of the performance of the DAM by computing upper bounds of the generalization error.