Generalized Additive Multi-Mixture Models for Data Mining

Themainideaofthispaperistomakestatisticalmodellingintoafeasibleandvaluableapproachto data mining. The class of generalized additive multi-models (GAM-M) is considered in the framework of non-linear regression methods and data mining. GAM-M are based on a combined model integration approach that aims to associate estimations derived from smoothing functions as well as by either parametric or non-parametric models. We extend this approach to provide a class of models based on a mixture model combination. Bootstrap averaging and model 􏲒t scoring are exploited in order to prevent over􏲒tting as well as to improve the prediction accuracy of the GAM-M models. The benchmarking of the proposed methodology is shown using a simulated data set