Classi er committee learning methods generate multiple classi ers to form a committee by repeated application of a single base learning algorithm. The committee members vote to decide the nal classication. Two such methods, Bagging and Boosting, have shown great success with decision tree learning. They create di erent classi ers by modifying the distribution of the training set. This paper stu...

Classiier learning is a key technique for KDD. Approaches to learning classiier committees, including Boosting, Bagging, Sasc, and SascB, have demonstrated great success in increasing the prediction accuracy of decision trees. Boosting and Bagging create diierent classiiers by modifying the distribution of the training set. Sasc adopts a diierent method. It generates committees by stochastic ma...

Two learning ensemble methods, Bagging and Boosting, have been applied to decision trees to improve classification accuracy over that of a single decision tree learner. We introduce Bagging and propose a variant of it — Improved Bagging — which, in general, outperforms the original bagging algorithm. We experiment on 22 datasets from the UCI repository, with emphasis on the ensemble’s accuracy ...

-Classification is one of the data mining techniques that analyses a given data set and induces a model for each class based on their features present in the data. Bagging and boosting are heuristic approaches to develop classification models. These techniques generate a diverse ensemble of classifiers by manipulating the training data given to a base learning algorithm. They are very successfu...

advanced data mining techniques can be used in universities classification, discovering specific patterns in the determination of successful students, design of a plan or a teaching method and finding critical points of financial management. in this article, we proposed a method to predict the rate of student enrollment in coming years. the data for this research were from data sets of voluntee...