基于分类示例的FlowSort参数提取

Q4 Business, Management and Accounting
Dimitri Van Assche, Y. D. Smet
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引用次数: 10

摘要

在多标准排序方法中,决策者通常很难精确地定义他们的偏好。将它们表示为参数值就更难了。这项工作的思想是在传统排序和区间排序的背景下,使用分类示例自动找到排序模型的参数。本文定义了区间排序,即方案可能被分配到几个连续的类别中。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,我们提出了一种基于遗传算法(GA)的启发式方法来识别权重,无差异和偏好阈值,以及表征类别的概况。在这两种情况下,我们在三个标准数据集上演示了算法的性能和解决方案的质量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
FlowSort parameters elicitation based on categorisation examples
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.
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来源期刊
International Journal of Multicriteria Decision Making
International Journal of Multicriteria Decision Making Business, Management and Accounting-Strategy and Management
CiteScore
0.70
自引率
0.00%
发文量
9
期刊介绍: IJMCDM is a scholarly journal that publishes high quality research contributing to the theory and practice of decision making in ill-structured problems involving multiple criteria, goals and objectives. The journal publishes papers concerning all aspects of multicriteria decision making (MCDM), including theoretical studies, empirical investigations, comparisons and real-world applications. Papers exploring the connections with other disciplines in operations research and management science are particularly welcome. Topics covered include: -Artificial intelligence, evolutionary computation, soft computing in MCDM -Conjoint/performance measurement -Decision making under uncertainty -Disaggregation analysis, preference learning/elicitation -Group decision making, multicriteria games -Multi-attribute utility/value theory -Multi-criteria decision support systems and knowledge-based systems -Multi-objective mathematical programming -Outranking relations theory -Preference modelling -Problem structuring with multiple criteria -Risk analysis/modelling, sensitivity/robustness analysis -Social choice models -Theoretical foundations of MCDM, rough set theory -Innovative applied research in relevant fields
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