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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 29 / No.1: The Twenty-Ninth Conference on Artificial Intelligence

Bayesian Model Averaging Naive Bayes (BMA-NB): Averaging over an Exponential Number of Feature Models in Linear Time

March 8, 2023

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Authors

Ga Wu

Australian National University


Scott Sanner

NICTA and Australian National University


Rodrigo Oliveira

University of Pernambuco


DOI:

10.1609/aaai.v29i1.9634


Abstract:

Naive Bayes (NB) is well-known to be a simple but effective classifier, especially when combined with feature selection. Unfortunately, feature selection methods are often greedy and thus cannot guarantee an optimal feature set is selected. An alternative to feature selection is to use Bayesian model averaging (BMA), which computes a weighted average over multiple predictors; when the different predictor models correspond to different feature sets, BMA has the advantage over feature selection that its predictions tend to have lower variance on average in comparison to any single model. In this paper, we show for the first time that it is possible to exactly evaluate BMA over the exponentially-sized powerset of NB feature models in linear-time in the number of features; this yields an algorithm about as expensive to train as a single NB model with all features, but yet provably converges to the globally optimal feature subset in the asymptotic limit of data. We evaluate this novel BMA-NB classifier on a range of datasets showing that it never underperforms NB (as expected) and sometimes offers performance competitive (or superior) to classifiers such as SVMs and logistic regression while taking a fraction of the time to train.

Topics: AAAI

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HOW TO CITE:

Ga Wu|| Scott Sanner|| Rodrigo Oliveira Bayesian Model Averaging Naive Bayes (BMA-NB): Averaging over an Exponential Number of Feature Models in Linear Time Proceedings of the AAAI Conference on Artificial Intelligence, 29 (2015) .

Ga Wu|| Scott Sanner|| Rodrigo Oliveira Bayesian Model Averaging Naive Bayes (BMA-NB): Averaging over an Exponential Number of Feature Models in Linear Time AAAI 2015, .

Ga Wu|| Scott Sanner|| Rodrigo Oliveira (2015). Bayesian Model Averaging Naive Bayes (BMA-NB): Averaging over an Exponential Number of Feature Models in Linear Time. Proceedings of the AAAI Conference on Artificial Intelligence, 29, .

Ga Wu|| Scott Sanner|| Rodrigo Oliveira. Bayesian Model Averaging Naive Bayes (BMA-NB): Averaging over an Exponential Number of Feature Models in Linear Time. Proceedings of the AAAI Conference on Artificial Intelligence, 29 2015 p..

Ga Wu|| Scott Sanner|| Rodrigo Oliveira. 2015. Bayesian Model Averaging Naive Bayes (BMA-NB): Averaging over an Exponential Number of Feature Models in Linear Time. "Proceedings of the AAAI Conference on Artificial Intelligence, 29". .

Ga Wu|| Scott Sanner|| Rodrigo Oliveira. (2015) "Bayesian Model Averaging Naive Bayes (BMA-NB): Averaging over an Exponential Number of Feature Models in Linear Time", Proceedings of the AAAI Conference on Artificial Intelligence, 29, p.

Ga Wu|| Scott Sanner|| Rodrigo Oliveira, "Bayesian Model Averaging Naive Bayes (BMA-NB): Averaging over an Exponential Number of Feature Models in Linear Time", AAAI, p., 2015.

Ga Wu|| Scott Sanner|| Rodrigo Oliveira. "Bayesian Model Averaging Naive Bayes (BMA-NB): Averaging over an Exponential Number of Feature Models in Linear Time". Proceedings of the AAAI Conference on Artificial Intelligence, 29, 2015, p..

Ga Wu|| Scott Sanner|| Rodrigo Oliveira. "Bayesian Model Averaging Naive Bayes (BMA-NB): Averaging over an Exponential Number of Feature Models in Linear Time". Proceedings of the AAAI Conference on Artificial Intelligence, 29, (2015): .

Ga Wu|| Scott Sanner|| Rodrigo Oliveira. Bayesian Model Averaging Naive Bayes (BMA-NB): Averaging over an Exponential Number of Feature Models in Linear Time. AAAI[Internet]. 2015[cited 2023]; .


ISSN: 2374-3468


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