论文标题
部分可观测时空混沌系统的无模型预测
StreamSoNG: A Soft Streaming Classification Approach
论文作者
论文摘要
检查大多数流群集群算法会导致理解它们实际上是增量分类模型。他们通过我们称之为足迹的摘要信息对现有和新发现的结构进行建模。 Incoming data is normally assigned a crisp label (into one of the structures) and that structure's footprint is incrementally updated.没有理由这些任务需要清晰。在本文中，我们提出了一种新的流分类算法，该算法使用神经气体原型作为足迹，并为每个传入载体生成一个可能的标签矢量（典型性）。这些典型性是由修改的可能的k-neareb邻居算法产生的。 The approach is tested on synthetic and real image datasets.我们将我们的方法与基于自适应随机森林，非常快速的决策规则以及次流算法的其他三个流媒体分类器进行了比较。
Examining most streaming clustering algorithms leads to the understanding that they are actually incremental classification models. They model existing and newly discovered structures via summary information that we call footprints. Incoming data is normally assigned a crisp label (into one of the structures) and that structure's footprint is incrementally updated. There is no reason that these assignments need to be crisp. In this paper, we propose a new streaming classification algorithm that uses Neural Gas prototypes as footprints and produces a possibilistic label vector (of typicalities) for each incoming vector. These typicalities are generated by a modified possibilistic k-nearest neighbor algorithm. The approach is tested on synthetic and real image datasets. We compare our approach to three other streaming classifiers based on the Adaptive Random Forest, Very Fast Decision Rules, and the DenStream algorithm with excellent results.
