Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

A Bayesian Nonparametric Learning Approach to Ensemble Models Using the Proper Bayesian Bootstrap

Version 1 : Received: 9 December 2020 / Approved: 11 December 2020 / Online: 11 December 2020 (10:22:05 CET)

A peer-reviewed article of this Preprint also exists.

Galvani, M.; Bardelli, C.; Figini, S.; Muliere, P. A Bayesian Nonparametric Learning Approach to Ensemble Models Using the Proper Bayesian Bootstrap. Algorithms 2021, 14, 11. Galvani, M.; Bardelli, C.; Figini, S.; Muliere, P. A Bayesian Nonparametric Learning Approach to Ensemble Models Using the Proper Bayesian Bootstrap. Algorithms 2021, 14, 11.

Journal reference: Algorithms 2021, 14, 11
DOI: 10.3390/a14010011

Abstract

Bootstrap resampling techinques, introduced by Efron and Rubin, can be presented in a general Bayesian framework, approximating the statistical distribution of a statistical functional φ(F), where F is a random distribution function. Efron’s and Rubin’s bootstrap procedures can be extended introducing an informative prior through the Proper Bayesian bootstrap. In this paper different bootstrap techniques are used and compared in predictive classification and regression models based on ensemble approaches, i.e. bagging models involving decision trees. Proper Bayesian bootstrap, proposed by Muliere and Secchi, is used to sample the posterior distribution over trees, introducing prior distributions on the covariates and the target variable. The results obtained are compared with respect to other competitive procedures employing different bootstrap techniques. The empirical analysis reports the results obtained on simulated and real data.

Subject Areas

Bootstrap; Bayesian nonparamteric learning; Ensemble Models

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