The Bayes Classifier Based On The Normal Mixture Model And Its Application

This paper mainly studies the Bayes classifier based on the normal mixture model for the classification.The Bayes classifier's criterion is maximum the posterior probabil-ity and the posterior probability needs to estimate the conditional distribution.For the continuous data of classification,it consists of multi-class and difficult to be described only by a single distribution.Under this situation,mixture model is a better choice and it can be obtained by the EM algorithm.By the simulation study,the Bayes classifier based on the normal mixture model is feasible and effective.For the multi-feature's classification,different features have different effects.This paper firstly sets up basic classifiers by the Bayes classifier based on the normal mixture model of each feature,then combines the idea of ensemble learning,gives a weight for each basic classifier by AdaBoost algorithm.The final classification is a linear combination of these basic classifiers.By testing on the actual wine data set of UCI database,it shows that our classification method combines with the ensemble learning can get high precision and robust classification.