Neural Comput - An extension of the receiver operating characteristic curve and AUC-optimal classification.

Tópicos

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Resumo

While most proposed methods for solving classification problems focus on minimization of the classification error rate, we are interested in the receiver operating characteristic (ROC) curve, which provides more information about classification performance than the error rate does. The area under the ROC curve (AUC) is a natural measure for overall assessment of a classifier based on the ROC curve. We discuss a class of concave functions for AUC maximization in which a boosting-type algorithm including RankBoost is considered, and the Bayesian risk consistency and the lower bound of the optimum function are discussed. A procedure derived by maximizing a specific optimum function has high robustness, based on gross error sensitivity. Additionally, we focus on the partial AUC, which is the partial area under the ROC curve. For example, in medical screening, a high true-positive rate to the fixed lower false-positive rate is preferable and thus the partial AUC corresponding to lower false-positive rates is much more important than the remaining AUC. We extend the class of concave optimum functions for partial AUC optimality with the boosting algorithm. We investigated the validity of the proposed method through several experiments with data sets in the UCI repository.

Resumo Limpo

propos method solv classif problem focus minim classif error rate interest receiv oper characterist roc curv provid inform classif perform error rate area roc curv auc natur measur overal assess classifi base roc curv discuss class concav function auc maxim boostingtyp algorithm includ rankboost consid bayesian risk consist lower bound optimum function discuss procedur deriv maxim specif optimum function high robust base gross error sensit addit focus partial auc partial area roc curv exampl medic screen high trueposit rate fix lower falseposit rate prefer thus partial auc correspond lower falseposit rate much import remain auc extend class concav optimum function partial auc optim boost algorithm investig valid propos method sever experi data set uci repositori

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