Logitboost of Simple Bayesian Classifier
Sotiris B. Kotsiantis, Panayiotis Pintelas · 2005
The ensembles of simple Bayesian classifiers have traditionally not been a focus of research. The reason is that simple Bayes is an extremely stable learning algorithm and most ensemble techniques such as bagging is mainly variance reduction techniques, thus not being able to benefit from its integration. However, simple Bayes can be effectively used in ensemble techniques, which perform also bias reduction, such as Logitboost. However, Logitboost requires a regression algorithm for base learner. For this reason, we slightly modify simple Bayesian classifier in order to be able to run as a regression method. Finally, we performed a large-scale comparison on 27 standard benchmark datasets with other state-of-the-art algorithms and ensembles using the simple Bayesian algorithm as base learner and the proposed technique was more accurate in most cases.