论文标题
无监督的模糊ex：不断发展的内部外部模糊聚类
Unsupervised Fuzzy eIX: Evolving Internal-eXternal Fuzzy Clustering
论文作者
论文摘要
随着时变的分类器，即不断发展的分类器，在信息可以作为永无止境的在线数据流中获取的情况下起着重要作用。我们提出了一种新的无监督学习方法，用于数值数据，称为进化的内部外部模糊聚类方法（模糊EIX）。我们提出了双边界模糊颗粒的概念，并详细说明了它的含义。 1型和2型模糊推理系统可以从模糊的EIX颗粒的投影中获得。我们执行模糊EIX分类器中平衡信息粒度的原理，以实现更高水平的模型可理解性。内部和外部颗粒是从数值数据流进行更新的，同时分类器的全局颗粒结构是自主进化的。一个称为双高斯人旋转的合成非组织问题显示了分类器的行为。模糊的EIX分类器可以在脱机训练的分类器显然会大大降低其准确性的情况下跟上其准确性。
Time-varying classifiers, namely, evolving classifiers, play an important role in a scenario in which information is available as a never-ending online data stream. We present a new unsupervised learning method for numerical data called evolving Internal-eXternal Fuzzy clustering method (Fuzzy eIX). We develop the notion of double-boundary fuzzy granules and elaborate on its implications. Type 1 and type 2 fuzzy inference systems can be obtained from the projection of Fuzzy eIX granules. We perform the principle of the balanced information granularity within Fuzzy eIX classifiers to achieve a higher level of model understandability. Internal and external granules are updated from a numerical data stream at the same time that the global granular structure of the classifier is autonomously evolved. A synthetic nonstationary problem called Rotation of Twin Gaussians shows the behavior of the classifier. The Fuzzy eIX classifier could keep up with its accuracy in a scenario in which offline-trained classifiers would clearly have their accuracy drastically dropped.
