有序分类的有效性度量：形式性质和实验结果
An Effectiveness Metric for Ordinal Classification: Formal Properties and Experimental Results
摘要：
在有序分类任务中，必须将项目分配到具有相对排序的类，例如情感分析中的积极、中性、消极。值得注意的是，最流行的顺序分类任务的评估指标要么忽略相关信息（例如，每个类的精确度/召回率忽略了它们的相对顺序），要么假设额外的信息（例如，平均误差假设类之间的绝对距离）。本文在测度论和信息论的基础上，提出了一种新的有序分类度量--贴近度评价测度。我们对合成数据和NLP共享任务数据的理论分析和实验结果表明，所提出的度量同时捕获不同传统任务的质量方面。此外，它概括了一些流行的分类（名义规模）和误差最小化（区间规模）的指标，这取决于它被实例化的测量规模。
英文摘要：
In Ordinal Classification tasks, items have to be assigned to classes that have a relative ordering, such as positive, neutral, negative in sentiment analysis. Remarkably, the most popular evaluation metrics for ordinal classification tasks either ignore relevant information (for instance, precision/recall on each of the classes ignores their relative ordering) or assume additional information (for instance, Mean Average Error assumes absolute distances between classes). In this paper we propose a new metric for Ordinal Classification, Closeness Evaluation Measure, that is rooted on Measurement Theory and Information Theory. Our theoretical analysis and experimental results over both synthetic data and data from NLP shared tasks indicate that the proposed metric captures quality aspects from different traditional tasks simultaneously. In addition, it generalizes some popular classification (nominal scale) and error minimization (interval scale) metrics, depending on the measurement scale in which it is instantiated.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
