分段延迟代价敏感三支决策
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模式识别与人工智能

通讯作者:

苗夺谦,dqmiao@tongji.edu.cn

中图分类号:

TP18

基金项目:

国家自然科学基金(61763031,61673301,61976158);江西省自然科学基金(20202BAB202018)


Piece-wise Delay Cost-sensitive Three-way Decisions
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    摘要:

    决策粗糙集理论中,三支决策代价目标函数是典型的单调线性函数.然而,在实践经验中经常发现延迟决策的代价与决策概率之间的函数关系往往呈现非单调特性,决策粗糙集理论的经典代价敏感三支决策模型无法对上述非单调现象进行直接的建模和推理,导致决策粗糙集理论的应用受到了限制.为了求解这种具有非单调延迟代价的代价敏感三支决策问题,提出一种新型分段延迟代价敏感三支决策模型.该模型定义了具有单调递增和单调递减特性的两组延迟决策损失函数,并结合经典正负域决策损失函数构造了分段延迟三支决策代价目标函数体系、度量指标和分段决策策略;然后,基于条件概率、损失函数及基础度量指标之间关系的4种分段延迟代价敏感三支决策分类模式被提了出来,并且对相应的三支分类阈值进行了推理;最后,通过一组典型实例,验证了分段延迟代价敏感三支决策模型及其三支分类是可行的.

    Abstract:

    In classic decision-theoretic rough sets (DTRS), the cost objective function of three-way decision is typical monotone linear function. However, it is usually found that the functional relationship between delay decision's cost and probability is non monotonic in practical experience. Hence, the classical cost sensitive three-way decision model in DTRS is not suitable for modeling and reasoning this non monotonic phenomenon. In order to solve the non-monotonic phenomena in the cost sensitive three-way decision problem, a novel piece-wise delay cost sensitive three-way decision model is proposed based on the classical positive/negative domain decision loss functions. The novel model defines two different sets of delay decision loss functions which have the characteristics of monotonous increase and monotonic decrease, and constructs segmented delay three-way decision cost objective function systems, measurement indexes, and segment decision strategies. Then, on the basis of the relationship among conditional probability, loss function and basic metrics, segmented delay cost sensitivity three-way decision model is proposed, and the corresponding three-way classification thresholds reasoning are implemented. Finally, a group of typical analysis examples are used to verify the feasibility of the segmented delay cost sensitivity three-way decision model and its classification.

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引用本文

徐健锋,苗夺谦,张远健.分段延迟代价敏感三支决策.软件学报,2022,33(10):3754-3775

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历史
  • 收稿日期:2020-04-04
  • 最后修改日期:2020-09-08
  • 录用日期:
  • 在线发布日期: 2022-10-13
  • 出版日期: 2022-10-06
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