不正确的多变量接收者工作特征(iMROC)曲线

S. Balaswamy, R. V. Vardhan, G. Sameera
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引用次数: 0

摘要

在多变量设置中,分类技术在识别个体/观察者的确切状态以及测试的准确性方面具有重要意义。其中一种分类技术是多元接收者工作特征曲线(Multivariate Receiver Operating Characteristic, MROC)。当随机分类器(猜测线)上的曲线满足其所有性质,特别是增加似然比函数的性质时,该技术以解释正确分类的程度而闻名。然而,在某些情况下,曲线违反了上述性质。这样的曲线称为反常曲线。本文论述了MROC曲线不合理的方法学及测量方法。使用实际数据集解释了该方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Improper Multivariate Receiver Operating Characteristic (iMROC) Curve
In a multivariate setup, the classification techniques have its significance in identifying the exact status of the individual/observer along with accuracy of the test. One such classification technique is the Multivariate Receiver Operating Characteristic (MROC) Curve. This technique is well known to explain the extent of correct classification with the curve above the random classifier (guessing line) when it satisfies all of its properties especially the property of increasing likelihood ratio function. However, there are circumstances where the curve violates the above property. Such a curve is termed as improper curve. This paper demonstrates the methodology of improperness of the MROC Curve and ways of measuring it. The methodology is explained using real data sets.
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