A hybrid ensemble of machine and statistical learning using confidence-based boosting

Nowadays, the classification problems have become more challenging due to the various types of data set. Some data are appropriated for machine learning techniques and some data are appropriated for statistical leaning techniques. This work proposes a new hybrid ensemble of machine and statistical learning models using confidence-based boosting. The proposed method which uses variants of based classifiers can solve classification problems in variant data set. Moreover, combining the confidence value to the current boosting method can improve the performance of classification. The performance of proposed method is compared to the ensemble of decision trees and MRN created by Adaboost.M1 on data sets from UCI. The experimental results show that the proposed method can improve the accuracy in both binary and multiclass classification problems.