Naive Feature Selection: Sparsity in Naive Bayes
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Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics, PMLR 108:18131822, 2020.
Abstract
Due to its linear complexity, naive Bayes classification remains an attractive supervised learning method, especially in very largescale settings. We propose a sparse version of naive Bayes, which can be used for feature selection. This leads to a combinatorial maximumlikelihood problem, for which we provide an exact solution in the case of binary data, or a bound in the multinomial case. We prove that our bound becomes tight as the marginal contribution of additional features decreases. Both binary and multinomial sparse models are solvable in time almost linear in problem size, representing a very small extra relative cost compared to the classical naive Bayes. Numerical experiments on text data show that the naive Bayes feature selection method is as statistically effective as stateoftheart feature selection methods such as recursive feature elimination, l_1penalized logistic regression and LASSO, while being orders of magnitude faster. For a large data set, having more than with 1.6 million training points and about 12 million features, and with a nonoptimized CPU implementation, our sparse naive Bayes model can be trained in less than 15 seconds.
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