论文标题
模糊积分=上下文线性顺序统计
Fuzzy Integral = Contextual Linear Order Statistic
论文作者
论文摘要
模糊积分是一个强大的参数非线函数，在各种应用中，从信息融合到分类，回归，决策，插值，指标，形态等等。虽然模糊积分通常是非线性运算符，但本文我们表明它可以由一组上下文线性订单统计（LOS）表示。可以通过采样模糊度量来获得这些操作员，并使用聚类来生成线性凸出总和的基础空间的分区。我们方法的好处包括可伸缩性，改进的积分/措施获取，可推广性和可解释/可解释的模型。我们的方法都在受控的合成实验上证明，并且还通过实际基准数据集进行了分析和验证。
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.
