用于带标签不确定性传感器融合的双容量Choquet积分
Bi-capacity Choquet Integral for Sensor Fusion with Label Uncertainty
AI总结：
本文提出Bi-MIChI框架，利用双容量Choquet积分在双极性尺度上建模传感器子集交互，并结合多示例学习处理标签不确定性，在合成与真实实验中验证了融合分类和检测性能。
摘要：
传感器融合将来自多个传感器源的数据进行组合，以提高数据解释的可靠性、鲁棒性和准确性。模糊积分（FI），特别是Choquet积分（ChI），常被用作跨多个传感器进行融合的强大非线性聚合器。然而，现有的有监督ChI学习算法通常要求每个输入数据点具有精确的训练标签，而这些标签可能难以或无法获得。此外，先前关于ChI融合的工作往往仅基于归一化模糊测度，这会将模糊测度值限制在[0, 1]范围内。当输入数据源的底层尺度是双极性的（即在[-1, 1]之间）时，这种限制可能会造成约束。为应对这些挑战，本文提出了一种新颖的基于Choquet积分的融合框架，名为Bi-MIChI（发音为“bi-mi-kee”），该框架使用双容量（bi-capacities）在双极性尺度上表示输入传感器源子集对之间的交互。这允许传感器源之间扩展的非线性交互，并可能产生有趣的融合结果。Bi-MIChI还通过多示例学习（Multiple Instance Learning）来处理标签不确定性，其中训练标签被应用于数据“包”（集合）而非每个实例。我们提出的Bi-MIChI框架在带标签不确定性的传感器融合的合成实验和真实世界实验中均展示了有效的分类和检测性能。我们还对模糊测度的行为进行了详细分析，以展示我们的融合过程。
英文摘要：
Sensor fusion combines data from multiple sensor sources to improve reliability, robustness, and accuracy of data interpretation. The Fuzzy Integral (FI), in particular, the Choquet integral (ChI), is often used as a powerful nonlinear aggregator for fusion across multiple sensors. However, existing supervised ChI learning algorithms typically require precise training labels for each input data point, which can be difficult or impossible to obtain. Additionally, prior work on ChI fusion is often based only on the normalized fuzzy measures, which bounds the fuzzy measure values between [0, 1]. This can be limiting in cases where the underlying scales of input data sources are bipolar (i.e., between [-1, 1]). To address these challenges, this paper proposes a novel Choquet integral-based fusion framework, named Bi-MIChI (pronounced "bi-mi-kee"), which uses bi-capacities to represent the interactions between pairs of subsets of the input sensor sources on a bi-polar scale. This allows for extended non-linear interactions between the sensor sources and can lead to interesting fusion results. Bi-MIChI also addresses label uncertainty through Multiple Instance Learning, where training labels are applied to "bags" (sets) of data instead of per-instance. Our proposed Bi-MIChI framework shows effective classification and detection performance on both synthetic and real-world experiments for sensor fusion with label uncertainty. We also provide detailed analyses on the behavior of the fuzzy measures to demonstrate our fusion process.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
