arXiv cs.AI· Eda Ersek Uyanik·· 10 小时前AI 评分12
多准则分类模型参数引导：NCS 模型直接引导方法研究
Elicitation of the parameters of a multi-criteria sorting model
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针对非补偿性分类模型（NCS）的参数引导问题，该博士论文提出了一套严格的直接引导流程，覆盖两类别、无否决的基本版本。对充分准则集，作者将提问算法嫁接于深度优先搜索等图探索算法并做仿真评估；对分界轮廓则采用二分法提问算法，并实现评估了 Hansel 提出的最坏情况最优算法及最多四准则下的平均最优决策树策略。论文还比较了 NCS 与 MR-Sort 的泛化程度。
正文 · 原文
Abstract:Multiple-criteria decision aiding (MCDA) proposes models and methods for different types of decision problems such as choice, ranking or sorting. Such models involve various parameters that must be elicited. In the elicitation process, all necessary information has to be taken from the decision-maker. For this, several direct and indirect elicitation methods are suggested in the literature. In this work, we concentrate on direct elicitation: we aim at providing a rigorous elicitation procedure for the parameters of the Non-Compensatory Sorting model (NCS), which has been characterized by an axiomatic analysis. A basic version of the NCS model is considered, i.e., a model with two categories and no vetoes. The parameters are the set of sufficient coalitions of criteria and the profile separating the two categories. Regarding the sufficient coalitions, we propose several questioning algorithms that engraft questions on classical graph exploration algorithms such as depth-first search. Such algorithms are implemented and evaluated by simulation. For eliciting the limit profile, we design a questioning algorithm using dichotomy. Questioning algorithms can be optimal in the worst case or on average. An optimal algorithm in the worst case has been proposed by Hansel in the framework of monotone Boolean functions. We implement and evaluate this algorithm as a variant of the depth-first search approaches. Regarding optimal questioning strategies on average, we evaluate decision trees for up to four criteria. On the other hand, we assess how more general is NCS as compared to MR-Sort, a particular case of the NCS model in which sufficient coalitions can be represented by additive weights.
| Comments: | PhD Thesis defended at University of Mons, Belgium, July 07 2023. 216 pages this https URL |
| Subjects: | Artificial Intelligence (cs.AI); Computer Science and Game Theory (cs.GT) |
| MSC classes: | 90B50 (Primary) 91B06, 91B08, 06A07 (Secondary) |
| ACM classes: | G.2.1; G.2.3; J.4 |
| Cite as: | arXiv:2611.13062 [cs.AI] |
| (or arXiv:2611.13062v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2611.13062 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Eda Ersek Uyanik [view email]
[v1]
Mon, 9 Nov 2026 17:49:26 UTC (12,379 KB)
来源：arXiv cs.AI · arxiv.org