Abstract: We presented a novel method for constructing ensembles of I3ayes-based classifiers called PCABoost, For creating a training data, our method splited the features set into K-subsets randomly, and applied principal component analysis to each of the feature subsets to get its corresponding principal components. And then all of principal components were put together to form a new feature space into which the total original dataset were mapped to create a new training set. Different process could generated different feature space and different training sets. On each of the new training data we generated a group of classifiers which were boosted one by one using Adal3oost, so we could generate several different classifiers groups in the several different feature spaces. In the classification phase we firstly got several predicts using weighted-voted inside each of the classifiers groups, and then voted on the several predicts to get the final result as the ensembles predict. Experiments were carried on 30 benchmark datascts picked up randomly from the UCI Machine Learning Repository, the results indicate that our method not only improves the performance of I3ayes-based classifiers significantly, but also get higher accuracy on most of data sets than other ensemble methods such as Rotation Forest and AdaBoost.

Key words: Classifier ensemble, Principal component analysis, Adal3oost, Bayes