A comparison of model aggregation methods for regression Export

@article{citeulike:3832275, abstract = {Combining machine learning models is a means of improving overall accuracy.Various algorithms have been proposed to create aggregate models from other models, and two popular examples for classification are Bagging and AdaBoost. In this paper we examine their adaptation to regression, and benchmark them on synthetic and real-world data. Our experiments reveal that different types of AdaBoost algorithms require different complexities of base models. They outperform Bagging at their best, but Bagging achieves a consistent level of success with all base models, providing a robust alternative.}, author = {Barutcuoglu, Zafer and Alpaydın, Ethem}, citeulike-article-id = {3832275}, journal = {LNCS}, keywords = {adaboost, aggregation, bagging, model, regression}, pages = {76--83}, posted-at = {2008-12-29 04:49:46}, priority = {2}, title = {A comparison of model aggregation methods for regression}, volume = {2714}, year = {2003} }

TY - JOUR ID - citeulike:3832275 L3 - citeulike-article-id:3832275 N2 - Combining machine learning models is a means of improving overall accuracy.Various algorithms have been proposed to create aggregate models from other models, and two popular examples for classification are Bagging and AdaBoost. In this paper we examine their adaptation to regression, and benchmark them on synthetic and real-world data. Our experiments reveal that different types of AdaBoost algorithms require different complexities of base models. They outperform Bagging at their best, but Bagging achieves a consistent level of success with all base models, providing a robust alternative. JF - LNCS EP - 83 TI - A comparison of model aggregation methods for regression VL - 2714 SP - 76 KW - adaboost KW - aggregation KW - bagging KW - model KW - regression AU - Barutcuoglu, Zafer AU - Alpaydın, Ethem PY - 2003/// ER -