论文标题
通过二进制偏好与简单回归的序数回归：统计和实验观点
Ordinal Regression via Binary Preference vs Simple Regression: Statistical and Experimental Perspectives
论文作者
论文摘要
已经提出了使用锚定参考样品（ORAR）的序数回归，以自动预测输入刺激的主观平均意见评分（MOS）。 Orars通过将测试样本与每个预定的锚定参考样本配对来解决MOS预测问题。然后，使用训练有素的二进制分类器来预测哪种样品，测试或锚定在统计上更好。然后，使用二进制偏好决定的后代来预测测试样本的MOS。在本文中，提出了严格的框架，分析和实验，以证明Orars在简单的回归中是有利的。这项工作的贡献是：1）表明可以将传统的回归重新构成多个偏好测试以产生更好的性能，这可以通过实验中的模拟来确认； 2）将Orars推广到其他回归问题并验证其有效性； 3）提供一些可以确保正确应用Orars的条件。
Ordinal regression with anchored reference samples (ORARS) has been proposed for predicting the subjective Mean Opinion Score (MOS) of input stimuli automatically. The ORARS addresses the MOS prediction problem by pairing a test sample with each of the pre-scored anchored reference samples. A trained binary classifier is then used to predict which sample, test or anchor, is better statistically. Posteriors of the binary preference decision are then used to predict the MOS of the test sample. In this paper, rigorous framework, analysis, and experiments to demonstrate that ORARS are advantageous over simple regressions are presented. The contributions of this work are: 1) Show that traditional regression can be reformulated into multiple preference tests to yield a better performance, which is confirmed with simulations experimentally; 2) Generalize ORARS to other regression problems and verify its effectiveness; 3) Provide some prerequisite conditions which can insure proper application of ORARS.
