Dreiseitl S, Ohno-Machado L, Kittler H, Vinterbo S, Billhardt H, Binder M
Decision Systems Group, Brigham and Women's Hospital, Division of Health Sciences and Technology, Harvard Medical School, Massachusetts Institute of Technology, Boston, Massachusetts, USA.
J Biomed Inform. 2001 Feb;34(1):28-36. doi: 10.1006/jbin.2001.1004.
We analyze the discriminatory power of k-nearest neighbors, logistic regression, artificial neural networks (ANNs), decision tress, and support vector machines (SVMs) on the task of classifying pigmented skin lesions as common nevi, dysplastic nevi, or melanoma. Three different classification tasks were used as benchmarks: the dichotomous problem of distinguishing common nevi from dysplastic nevi and melanoma, the dichotomous problem of distinguishing melanoma from common and dysplastic nevi, and the trichotomous problem of correctly distinguishing all three classes. Using ROC analysis to measure the discriminatory power of the methods shows that excellent results for specific classification problems in the domain of pigmented skin lesions can be achieved with machine-learning methods. On both dichotomous and trichotomous tasks, logistic regression, ANNs, and SVMs performed on about the same level, with k-nearest neighbors and decision trees performing worse.
我们分析了k近邻算法、逻辑回归、人工神经网络（ANN）、决策树和支持向量机（SVM）在将色素沉着性皮肤病变分类为普通痣、发育异常痣或黑色素瘤任务中的辨别能力。使用了三种不同的分类任务作为基准：区分普通痣与发育异常痣和黑色素瘤的二分问题、区分黑色素瘤与普通痣和发育异常痣的二分问题，以及正确区分所有三类的三分问题。使用ROC分析来衡量这些方法的辨别能力表明，通过机器学习方法可以在色素沉着性皮肤病变领域的特定分类问题上取得优异结果。在二分和三分任务中，逻辑回归、人工神经网络和支持向量机的表现大致相同，k近邻算法和决策树的表现较差。
