保守决策与风险评分数据集
包含风险评分数据，用于前列腺癌诊断的保守分类决策，支持分类器弃权以优化准确率。
基本信息
资源简介
保守决策与风险评分数据集
【一句话概览】
本文提出基于风险评分的保守决策方法，在二分类中允许弃权。该方法确定风险评分的最优截断区间，区间内不作决策，区间外最大化分类准确率。其受支持向量机启发，但最小化而非最大化分类间隔（来源 figshare，CC BY 4.0 许可）。
【数据集背景】
本数据集源站标题为《Conservative Decisions with Risk Scores》。配套论文《Conservative Decisions with Risk Scores》发表于 Journal of Computational and Graphical Statistics（2026），DOI：10.1080/10618600.2026.2652932。项目资助方：This research was partially supported by the National Cancer Institute of the National Institutes of Health (NCI–NIH) under Award Numbers 1T32GM144919, R25GM069621, SC1CA245675, and 2U54MD007592.。源站首次发布：2026-04-01。
【数据内容】
源站原始描述：In binary classification applications, conservative decision-making that allows for abstention can be advantageous. To this end, we introduce a novel approach that determines the optimal cutoff interval for risk scores, which can be directly available or derived from fitted models. Within this interval, the algorithm refrains from making decisions, while outside the interval, classification accuracy is maximized. Our approach is inspired by support vector machines (SVM), but differs in that it m…
文件 README-Supp.txt 为文本内容，开头为：The online supplements consist of two components: 1. supp.pdf A supplementary document containing additional theoretical, algorithmic, and numerical result…
【数据结构与技术规格】
文件数 3 个，合计 6.76 MB。R-CD.zip：application/zip，274.40 KB。README-Supp.txt：text/plain，684 B。数据集 DOI：10.6084/m9.figshare.31919972.v1。
【主题与分类】
源站学科分类：Medicine、Biological Sciences not elsewhere classified、Information Systems not elsewhere classified、Mathematical Sciences not elsewhere classified。主题标签：Classification with the reject option、Convex optimization、Optimal cutoff point、Risk-coverage curve、Selective classification、Support vector machines (SVM)。
【适用方向】
结构化表格数据可直接用于统计建模、特征工程与队列分析。
【获取与许可】
源站页面：https://tandf.figshare.com/articles/dataset/Conservative_Decisions_with_Risk_Scores/31919972
使用许可：CC BY 4.0（https://creativecommons.org/licenses/by/4.0/）。
规范引用：Conservative Decisions with Risk Scores. Taylor & Francis. Wei, Yishu; Lee, Wen-Yee; Quaye, George Ekow; Su, Xiaogang
【使用注意】
· 源站最后修改于 2026-06-03，引用时请注意版本时效。
下载信息
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使用方式
数据集获取（Figshare）
- 源站标题：Conservative Decisions with Risk Scores
- DOI：10.6084/m9.figshare.31919972.v1
- 发布日期：2026-04-01
- 文件数：3 个
- 文件总体积：6.76 MB
- 源站页面：https://tandf.figshare.com/articles/dataset/Conservative_Decisions_with_Risk_Scores/31919972
命令行下载
curl -L -o "R-CD.zip" "https://ndownloader.figshare.com/files/63387905"
curl -L -o "README-Supp.txt" "https://ndownloader.figshare.com/files/63387908"
curl -L -o "supp.pdf" "https://ndownloader.figshare.com/files/63387911"
文件清单
R-CD.zip（274.40 KB）README-Supp.txt（684 B）supp.pdf（6.49 MB）
文件总体积 6.76 MB（源站 API 实测）。
数据说明
In binary classification applications, conservative decision-making that allows for abstention can be advantageous. To this end, we introduce a novel approach that determines the optimal cutoff interval for risk scores, which can be directly available or derived from fitted models. Within this interval, the algorithm refrains from making decisions, while outside the interval, classification accuracy is maximized. Our approach is inspired by support vector machines (SVM), but differs in that it minimizes the classification margin rather than maximizing it. We provide the theoretical optimal solution to this problem, which holds important practical implications. Our proposed method not only supports conservative decision-making but also inherently results in a risk-coverage curve. Together with its associated area under the curve, this representation offers a comprehensive performance metric for evaluating and comparing classifiers, akin to the receiver operating characteristic (ROC). To investigate and illustrate our approach, we conduct both simulation studies and a real-world case study in the context of diagnosing prostate cancer. Supplemental materials are available online.
许可
CC BY 4.0
引用
学术使用请引用 DOI 10.6084/m9.figshare.31919972.v1（source: https://tandf.figshare.com/articles/dataset/Conservative_Decisions_with_Risk_Scores/31919972）。
数据缺失？
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