Outlier Detection with One-Class SVMs: An Application to Melanoma Prognosis

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.

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.

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.

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.

One-class SVMs offer a viable alternative to two-class classification algorithms if class distribution is heavily imbalanced.