Boosted Gaussian Bayes Classifier and its application in bank credit scoring

With the explosion of computer science in the last decade, data banks and networks management present a huge part of tomorrows problems. One of them is the development of the best classi cation method possible in order to exploit the data bases. In classi cation problems, a representative successful method of the probabilistic model is a Naïve Bayes classi er. However, the Naïve Bayes e ectiveness still needs to be upgraded. Indeed, Naïve Bayes ignores misclassi ed instances instead of using it to become an adaptive algorithm. Di erent works have presented solutions on using Boosting to improve the Gaussian Naïve Bayes algorithm by combining Naïve Bayes classi er and Adaboost methods. But despite these works, the Boosted Gaussian Naïve Bayes algorithm is still neglected in the resolution of classi cation problems. One of the reasons could be the complexity of the implementation of the algorithm compared to a standard Gaussian Naïve Bayes. We present in this paper, one approach of a suitable solution with a pseudo-algorithm that uses Boosting and Gaussian Naïve Bayes principles having the lowest possible complexity.