将多维值扩展到包括分类属性
Jennifer A Whitty1, Nicolas Krucien2, Caitlin Thomas2
1Evidera, London UK; Norwich Medical School, University of East Anglia, Norwich UK; School of Pharmacy, University of Queensland, Brisbane, Australia.
概括
本研究引入了一种新方法,将分类属性纳入多维值 (MDT) 模型. 该方法允许分类数据与连续变量一起以最小的精度损失,扩展MDT应用.
科学领域:
- 决策科学 决策科学
- 营销科学 营销科学
- 心理测量 心理测量 心理测量
背景情况:
- 多维值 (MDT) 是一种很有价值的工具,可以引起个人偏好.
- 当前的MDT模型假定连续属性,限制它们的适用性与分类数据.
研究的目的:
- 提出一个新的框架,将分类属性纳入MDT.
- 概述一个设计MDT研究的过程,该过程中混合了分类和连续属性.
主要方法:
- 分类属性排名和分数的分数分配.
- 排名属性规模波动的重要性.
- 对于连续属性的值练习.
- 交易分类属性波动与连续属性波动对比.
主要成果:
- 在MDT中包含分类属性是可行的,但精度略有损失.
- 精度损失取决于分类属性的重要性和排名.
- 连续属性的数量和选择任务对精度的影响最小.
结论:
- 拟议的框架成功地将MDT扩展到包括分类属性.
- 当分类属性不是偏好的主要驱动因素时,这种方法特别有用.
- 建议在应用环境中进行进一步的研究.
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