AMIA Annu Symp Proc - Outlier Detection with One-Class SVMs: An Application to Melanoma Prognosis.

Tópicos

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Resumo

CKGROUND: Medical diagnosis and prognosis using machine learning methods is usually represented as a supervised classification problem, where a model is built to distinguish "normal" from "abnormal" cases. If cases are available from only one class, this approach is not feasible.OBJECTIVE: To evaluate the performance of classification via outlier detection by one-class support vector machines (SVMs) as a means of identifying abnormal cases in the domain of melanoma prognosis.METHODS: Empirical evaluation of one-class SVMs on a data set for predicting the presence or absence of metastases in melanoma patients, and comparison with regular SVMs and artificial neural networks.RESULTS: One-class SVMs achieve an area under the ROC curve (AUC) of 0.71; two-class algorithms achieve AUCs between 0.5 and 0.84, depending on the available number of cases from the minority class.CONCLUSION: One-class SVMs offer a viable alternative to two-class classification algorithms if class distribution is heavily imbalanced.

Resumo Limpo

ckground medic diagnosi prognosi use machin learn method usual repres supervis classif problem model built distinguish normal abnorm case case avail one class approach feasibleobject evalu perform classif via outlier detect oneclass support vector machin svms mean identifi abnorm case domain melanoma prognosismethod empir evalu oneclass svms data set predict presenc absenc metastas melanoma patient comparison regular svms artifici neural networksresult oneclass svms achiev area roc curv auc twoclass algorithm achiev auc depend avail number case minor classconclus oneclass svms offer viabl altern twoclass classif algorithm class distribut heavili imbalanc

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