使用单类支持向量机进行异常值检测：在黑色素瘤预后中的应用
Outlier Detection with One-Class SVMs: An Application to Melanoma Prognosis.
作者信息
Dreiseitl Stephan, Osl Melanie, Scheibböck Christian, Binder Michael
机构信息
Dept. of Software Engineering, Upper Austria University of Applied Sciences, Hagenberg, Austria.
出版信息
AMIA Annu Symp Proc. 2010 Nov 13;2010:172-6.
BACKGROUND
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.
背景
使用机器学习方法进行医学诊断和预后评估通常被表示为一个监督分类问题，即构建一个模型来区分“正常”和“异常”病例。如果仅能获取来自一个类别的病例，这种方法就不可行。
目的
评估通过单类支持向量机（SVM）进行异常检测的分类性能，以此作为识别黑色素瘤预后领域异常病例的一种手段。
方法
对用于预测黑色素瘤患者是否存在转移的数据集进行单类SVM的实证评估，并与常规SVM和人工神经网络进行比较。
结果
单类SVM的ROC曲线下面积（AUC）为0.71；二类算法的AUC在0.5至0.84之间波动，具体取决于少数类别的可用病例数量。
结论
如果类分布严重失衡，单类SVM为二类分类算法提供了一种可行的替代方案。
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