We present a method for constructing ensembles from libraries of thousands of models. Model libraries are generated using different learning algorithms and parameter settings. For...
Rich Caruana, Alexandru Niculescu-Mizil, Geoff Cre...
— Both theory and a wealth of empirical studies have established that ensembles are more accurate than single predictive models. Unfortunately, the problem of how to maximize ens...
We empirically evaluate several state-of-the-art methods for constructing ensembles of classifiers with stacking and show that they perform (at best) comparably to selecting the be...
We empirically evaluate several state-of-theart methods for constructing ensembles of heterogeneous classifiers with stacking and show that they perform (at best) comparably to se...
We describe a data mining framework that derives panelist information from sparse flavour survey data. One component of the framework executes genetic programming ensemble based s...