Generalized additive multi-mixture model for data mining

Authors:
Claudio Conversano;Roberta Siciliano;Francesco Mola
Affiliations:
Department of Mathematics and Statistics, University of Naples Federico II Via Cinthia, Monte S. Angelo, I-80126, Napoli, Italy;Department of Mathematics and Statistics, University of Naples Federico II Via Cinthia, Monte S. Angelo, I-80126, Napoli, Italy;Department of Economics, University of Cagliari Via Fra' Ignazio, I-90123, Cagliari, Italy
Venue:
Computational Statistics & Data Analysis - Nonlinear methods and data mining
Year:
2002

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Abstract

The main idea of this paper is to make statistical modelling into a feasible and valuable approach to 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 fit scoring are exploited in order to prevent overfitting 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.