Screening is a process of filtering out items that are less likely to be selected, so that decision makers can more easily focus on evaluating items that more likely to be chosen in the smaller set. The case-based distance method is a popular approach for screening. However, previous case-based distance methods usually minimized the overall squared error or the number of misclassifications, possibly leading to an increased number of misclassifications or the overall squared error, respectively. In addition, the efficiency of individual screening may be insufficient, especially when there are a large number of alternatives. This study develops a new visual group-based screening approach. Revised models to improve previous screening and displaying models are also constructed herein. The concept of similarity upper approximation is then employed to provide group-based screening solutions. Lastly, a new visualization model is proposed to simultaneously allocate, group and screen alternatives. Compared with previous methods, this approach produces the lowest misclassification rate, while simultaneously yielding the smallest squared error. The individual alternatives, relationships among alternatives and cases, grouping relationships, and the acceptable ring can be directly observed through visual presentation. In addition, the proposed approach can provide flexibility in that a decision maker can choose to employ group-based or individual-based screening.

筛选是过滤掉不太可能被选中的项目的过程，这样决策者就可以更容易地专注于评估在较小的集合中更有可能被选中的项目。基于案例的距离方法是一种流行的筛查方法。然而，以前基于案例的距离方法通常会最小化总体平方误差或错误分类的数量，可能分别导致错误分类数量或总体平方误差的增加。此外，单独筛选的效率可能不足，尤其是在有大量替代品的情况下。本研究开发了一种新的基于视觉组的筛选方法。此处还构建了用于改进先前筛选和显示模型的修订模型。然后采用相似性上近似的概念来提供基于组的筛选解决方案。最后，提出了一种新的可视化模型来同时分配、分组和筛选备选方案。与以前的方法相比，这种方法产生最低的误分类率，同时产生最小的平方误差。单个备选方案、备选方案与案例之间的关系、分组关系和可接受的环可以通过视觉呈现直接观察。此外，所提出的方法可以提供灵活性，因为决策者可以选择采用基于群体或基于个体的筛选。这种方法产生最低的误分类率，同时产生最小的平方误差。单个备选方案、备选方案与案例之间的关系、分组关系和可接受的环可以通过视觉呈现直接观察。此外，所提出的方法可以提供灵活性，因为决策者可以选择采用基于群体或基于个体的筛选。这种方法产生最低的误分类率，同时产生最小的平方误差。单个备选方案、备选方案与案例之间的关系、分组关系和可接受的环可以通过视觉呈现直接观察。此外，所提出的方法可以提供灵活性，因为决策者可以选择采用基于群体或基于个体的筛选。