Design Variety Measurement using Sharma-Mittal Entropy
Design Variety Measurement using Sharma-Mittal Entropy
复制标题
使用 Sharma-Mittal 熵设计品种测量
DOI：
10.1115/1.4048743
复制
发表时间：
2020
影响因子：
3.3
通讯作者：
Miller, Scarlett
中科院分区：
文献类型：
--
作者：
Ahmed, Faez;Ramachandran, Sharath Kumar;Fuge, Mark;Hunter, Samuel;Miller, Scarlett
设计多样性度量衡量了设计空间被探索的程度。本文提出了一个广义类的熵度量的基础上Sharma-Mittal熵提供了优于现有的方法来衡量设计的多样性。我们表明，一个来自Sharma-Mittal熵的范例度量，即Herfindahl-Hirschman设计指数（HHID）与现有度量相比具有以下可取的优点：（a）更准确：与现有和常用的基于树的两个新数据集的度量相比，它更好地与人类评级保持一致;（B）更高的灵敏度：与现有方法相比，它在区分各种集合时具有更高的灵敏度;（c）允许有效的优化：它是一个子模函数，这使得人们能够使用多项式时间贪婪算法来优化设计多样性;以及（d）推广到多个度量：可以通过改变该度量的参数来导出许多现有的度量，这允许研究人员拟合度量以更好地表示新领域的多样性。本文还提供了一个通过从成对比较构建地面真实数据集来比较用于测量多样性的度量的过程。总的来说，我们的研究结果揭示了良好的设计多样性指标应该具备的一些品质，以及与收集测量这些品质所需的数据相关的重要挑战。
Design variety metrics measure how much a design space is explored. This article proposes that a generalized class of entropy metrics based on Sharma–Mittal entropy offers advantages over existing methods to measure design variety. We show that an exemplar metric from Sharma–Mittal entropy, namely, the Herfindahl–Hirschman index for design (HHID) has the following desirable advantages over existing metrics: (a) more accuracy: it better aligns with human ratings compared to existing and commonly used tree-based metrics for two new datasets; (b) higher sensitivity: it has higher sensitivity compared to existing methods when distinguishing between the variety of sets; (c) allows efficient optimization: it is a submodular function, which enables one to optimize design variety using a polynomial time greedy algorithm; and (d) generalizes to multiple metrics: many existing metrics can be derived by changing the parameters of this metric, which allows a researcher to fit the metric to better represent variety for new domains. This article also contributes a procedure for comparing metrics used to measure variety via constructing ground truth datasets from pairwise comparisons. Overall, our results shed light on some qualities that good design variety metrics should possess and the nontrivial challenges associated with collecting the data needed to measure those qualities.