一种基于鲁棒方法的数据挖掘分析排序方法
A novel data envelopment analysis ranking based on a robust approach
摘要：
我们提出了一种新的DEA排名的基础上，强大的优化观点：较高的排名为那些DMU的，仍然有效，即使是较大的数据变化，反之亦然。这个排名可以通过解决广义线性分式规划问题来计算，但我们也提出了一个紧线性规划近似，保持排名的顺序。我们展示了我们的方法的一些显着的属性：它保留了排名的顺序相比，经典的方法。它是自然规范化的，因此它可以作为不相关模型的决策单元的通用排序。它不仅对低效的决策单元进行排序，而且对高效的决策单元进行排序。它也可以很容易地扩展到广义模型，例如处理区间数据。我们提出了几个例子，确认所需的属性的方法。
英文摘要：
We propose a novel DEA ranking based on a robust optimization viewpoint: the higher ranking for those DMU's that remain efficient even for larger variations of data and vice versa. This ranking can be computed by solving generalized linear fractional programming problems, but we also present a tight linear programming approximation that preserves the order of rankings. We show some remarkable properties of our approach: It preserves the order of rankings compared to the classical approach. It is naturally normalized, so it can be used as universal ranking of DMU's of unrelated models. It gives ranking not only for inefficient, but also for efficient decision making units. It can also be easily extended to generalized model, for instance to deal with interval data. We present several examples confirming the desirable properties of the method.
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