模糊积分=上下文线性顺序统计量
Fuzzy Integral = Contextual Linear Order Statistic
摘要：
模糊积分是一个功能强大的参数化非线性函数，具有广泛的应用，从信息融合到分类，回归，决策，插值，度量，形态学等。虽然模糊积分一般是一个非线性算子，在这里，我们表明，它可以表示为一组上下文的线性顺序统计量（LOS）。这些算子可以通过对模糊测度进行采样而获得，聚类用于产生线性凸和的底层空间的划分。我们的方法的好处包括可扩展性，改进的积分/测量采集，概括性和可解释/可解释的模型。我们的方法既在受控合成实验中进行了演示，又使用真实世界的基准数据集进行了分析和验证。
英文摘要：
The fuzzy integral is a powerful parametric nonlin-ear function with utility in a wide range of applications, from information fusion to classification, regression, decision making,interpolation, metrics, morphology, and beyond. While the fuzzy integral is in general a nonlinear operator, herein we show that it can be represented by a set of contextual linear order statistics(LOS). These operators can be obtained via sampling the fuzzy measure and clustering is used to produce a partitioning of the underlying space of linear convex sums. Benefits of our approach include scalability, improved integral/measure acquisition, generalizability, and explainable/interpretable models. Our methods are both demonstrated on controlled synthetic experiments, and also analyzed and validated with real-world benchmark data sets.
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。
