Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed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

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.

Keywords

Bootstrap; Bayesian nonparamteric learning; Ensemble Models

Subject

Computer Science and Mathematics, Algebra and Number Theory

Comments (0)

We encourage comments and feedback from a broad range of readers. See criteria for comments and our Diversity statement.

Leave a public comment
Send a private comment to the author(s)
* All users must log in before leaving a comment
Views 0
Downloads 0
Comments 0
Metrics 0


×
Alerts
Notify me about updates to this article or when a peer-reviewed version is published.
We use cookies on our website to ensure you get the best experience.
Read more about our cookies here.