Ensemble of decision trees based on representative data
Wang Li-qun · Jisuanji yingyong yanjiu · 2009
To generate better ensemble output of decision trees,based on the theoretic analysis,this paper put forward a method used for ensemble of decision trees with representative data from the data point of view.This method extracted representative data via partition around medoids(PAM) algorithm from the original training set at first,then it trained a number of decision trees with the help of the representative data and built a ensemble model with the trained decision trees.This method could select the less representative data and trained the better ensemble model of decision trees.The experiment results show that this method can obtain higher ensemble precision of decision trees than Bagging or Boosting furthermore it uses less representative training set.