Methods Inf Med - An experimental evaluation of boosting methods for classification.

Tópicos

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Resumo

JECTIVES: 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 non- linear 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.

Resumo Limpo

jectiv clinic medicin accuraci achiev classif rule often suffici justifi use daili practic order improv classifi becom popular combin singl classif rule classif ensembl two popular boost method will compar classic statist approachesmethod use data clinic studi diagnosi breast tumor simul will compar adaboost gradient boost ensembl regress tree will also consid tree approach logist regress tradit competitor logist regress allow select non linear effect fraction polynomi approach perform classifi will assess estim misclassif rate brier scoreresult will show boost simpl base classifi give classif rule improv predict abil howev perform boost classifi general superior perform logist regress contrast computerintens method latter base classifi much easier interpret useconclus medic applic logist regress model remain method choic least serious competitor sophist techniqu refin boost method use optim number boost step may lead improv

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