arXiv:2505.06516cs.CV2025-05被引 1

提出量子冲突指标,提升异常检测准确率

Quantum Conflict Measurement in Decision Making for Out-of-Distribution Detection

  • 引入量子冲突指标衡量多个模糊信息间的矛盾程度
  • 在异常检测任务中,AUC提升1.2%,误报率降低5.4%
  • 适合需要高可靠性决策的场景,如安全敏感系统

量子达姆斯特定理(QDST)利用量子干涉效应构建模糊度量的量子质量函数(QMF),并通过量子并行计算加速处理。然而,如何有效管理多个QMF之间的冲突仍是难题。本文提出量子冲突指示器(QCI),用于量化两份QMF在决策中的冲突程度,并系统分析其性质。结果表明,QCI满足非负性、对称性、有界性、极端一致性及对细化不敏感等理想冲突度量特性。将QCI应用于冲突融合,性能优于多种常用方法。进一步提出类描述域空间(C-DDS)及其优化版本C-DDS+,结合QCI融合策略,用于解决分布外(OOD)检测问题。实验显示,该方法在多个主流基线方法上表现更优,平均提升受试者工作特征曲线下面积(AUC)1.2%,在95%真阴性率下的假阳性率(FPR95)平均下降5.4%。

原文摘要 · Abstract (English)

Quantum Dempster-Shafer Theory (QDST) uses quantum interference effects to derive a quantum mass function (QMF) as a fuzzy metric type from information obtained from various data sources. In addition, QDST uses quantum parallel computing to speed up computation. Nevertheless, the effective management of conflicts between multiple QMFs in QDST is a challenging question. This work aims to address this problem by proposing a Quantum Conflict Indicator (QCI) that measures the conflict between two QMFs in decision-making. Then, the properties of the QCI are carefully investigated. The obtained results validate its compliance with desirable conflict measurement properties such as non-negativity, symmetry, boundedness, extreme consistency and insensitivity to refinement. We then apply the proposed QCI in conflict fusion methods and compare its performance with several commonly used fusion approaches. This comparison demonstrates the superiority of the QCI-based conflict fusion method. Moreover, the Class Description Domain Space (C-DDS) and its optimized version, C-DDS+ by utilizing the QCI-based fusion method, are proposed to address the Out-of-Distribution (OOD) detection task. The experimental results show that the proposed approach gives better OOD performance with respect to several state-of-the-art baseline OOD detection methods. Specifically, it achieves an average increase in Area Under the Receiver Operating Characteristic Curve (AUC) of 1.2% and a corresponding average decrease in False Positive Rate at 95% True Negative Rate (FPR95) of 5.4% compared to the optimal baseline method.

异常检测量子推理决策融合

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