单调分类：算法、性能度量和数据集综述
Monotonic classification: an overview on algorithms, performance measures and data sets
摘要：
目前，在数据库中的知识发现是一个重要的步骤，以确定有效的，新颖的和有用的模式决策。有许多现实世界的场景，如破产预测，期权定价或医疗诊断，其中要学习的分类模型需要满足单调性的限制（即当输入属性值增加时，目标类别标签不应减少）。例如，我们可以合理地假设，一家公司的负债率越高，其破产风险就越低。因此，有越来越多的兴趣从数据采掘研究社区有关单调预测模型。本文的目的是在该领域的文献，分析现有的技术，并提出了分类的算法的基础上生成的模型的类型的概述。对于每一种方法，我们审查的质量指标的评估和分析中使用的不同的数据集和单调问题。因此，本文对专业文献中单调分类的研究进行了综述，并可作为该领域的功能指南。
英文摘要：
Currently, knowledge discovery in databases is an essential step to identify valid, novel and useful patterns for decision making. There are many real-world scenarios, such as bankruptcy prediction, option pricing or medical diagnosis, where the classification models to be learned need to fulfil restrictions of monotonicity (i.e. the target class label should not decrease when input attributes values increase). For instance, it is rational to assume that a higher debt ratio of a company should never result in a lower level of bankruptcy risk. Consequently, there is a growing interest from the data mining research community concerning monotonic predictive models. This paper aims to present an overview about the literature in the field, analyzing existing techniques and proposing a taxonomy of the algorithms based on the type of model generated. For each method, we review the quality metrics considered in the evaluation and the different data sets and monotonic problems used in the analysis. In this way, this paper serves as an overview of the research about monotonic classification in specialized literature and can be used as a functional guide of the field.
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。
