An Experimental Evaluation of Boosting Methods for Classification

Abstract

Summary Objectives: In clinical medicine, the accuracy achieved by classification rules is often not sufficient to justify their use in daily practice. In order to improve classifiers it has become popular to combine single classification rules into a classification ensemble. Two popular boosting methods will be compared with classical statistical approaches. Methods: Using data from a clinical study on the diagnosis of breast tumors and by simulation we will compare AdaBoost with gradient boosting ensembles of regression trees. We will also consider a tree approach and logistic regression as traditional competitors. In logistic regression we allow to select nonlinear effects by the fractional polynomial approach. Performance of the classifiers will be assessed by estimated misclassification rates and the Brier score. Results: We will show that boosting of simple base classifiers gives classification rules with improved predictive ability. However, the performance of boosting classifiers was not generally superior to the performance of logistic regression. In contrast to the computer-intensive methods the latter are based on classifiers which are much easier to interpret and to use. Conclusions: In medical applications, the logistic regression model remains a method of choice or, at least, a serious competitor of more sophisticated techniques. Refinement of boosting methods by using optimized number of boosting steps may lead to further improvement.