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FlowSort parameters elicitation based on categorisation examples

机译:基于分类示例的FlowSort参数启发

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In multi-criteria sorting methods, it is often difficult for decision makers to precisely define their preferences. It is even harder to express them into parameters values. The idea of this work is to automatically find the parameters of a sorting model using classification examples in the contexts of traditional sorting and interval sorting. Interval sorting, i.e., the possible assignment of alternatives into several successive categories, is defined in this paper. The sorting method we are working with is FlowSort, which is based on the PROMETHEE methodology. Starting with an evaluation table and known allocations, we propose a heuristic based on a genetic algorithm (GA) to identify the weights, indifference and preference thresholds but also profiles characterising the categories. We illustrate both the performances of the algorithm and the quality of the solutions on three standard datasets in both cases.
机译:在多标准排序方法中,决策者通常很难精确定义其偏好。很难将它们表示为参数值。这项工作的想法是在传统排序和区间排序的上下文中使用分类示例自动找到排序模型的参数。本文定义了时间间隔排序,即将备选方案分配为几个连续类别的可能性。我们正在使用的排序方法是FlowSort,它基于PROMETHEE方法。从评估表和已知分配开始,我们提出一种基于遗传算法(GA)的启发式算法,以识别权重,冷漠和偏好阈值,还可以识别类别的特征。在这两种情况下,我们都在三个标准数据集上说明了算法的性能和解决方案的质量。

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