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Naïve Bayes (NB) classifiers are simple probabilistic classifiers still widely used in supervised learning due to their tradeoff between efficient model training and good empirical results. One of the drawbacks of these classifiers is that in situations of data sparsity (i.e. when the size of training set is small) the maximum likelihood estimation of the probability of unseen features in these situations is equal to zero causing arithmetic anomalies. To prevent this undesirable behavior, adoi:10.1145/2348283.2348427 dblp:conf/sigir/NunzioS12 fatcat:nyr4tspjdzcutnae3g3hhwzc74