Data classification and regression are commonly encountered data analysis problems. Many researchers created multiple tools to deal with these issues. Fuzzy clustering, fuzzy decision trees, and ensemble classifiers such as fuzzy forests are popular tools used for this kind of problems. We would like to describe some interesting, more or less popular, solutions which belong to mentioned areas to show the way they deal with data classification and regression problems. This paper is divided into four parts. In the first part we present the issue of fuzzy clustering, which is one of the most important aspects of fuzzy trees which base on clusters. Some methods of splitting objects into clusters using fuzzy logic are described there. The second part describes different fuzzy decision trees. The way these trees can deal with classification and regression problems is presented. In the third part the issue of forests—ensemble classifiers which consist of fuzzy trees—is described. The last part treats about the way of performing weighted decision making in fuzzy forests.

中文翻译：

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模糊的树木和森林-审查

数据分类和回归是常见的数据分析问题。许多研究人员创建了多种工具来处理这些问题。模糊聚类，模糊决策树和诸如模糊森林之类的整体分类器是用于此类问题的流行工具。我们想描述一些有趣的，或多或少受欢迎的解决方案，它们属于上述领域，以显示它们处理数据分类和回归问题的方式。本文分为四个部分。在第一部分中，我们提出了模糊聚类的问题，这是基于聚类的模糊树的最重要方面之一。那里描述了一些使用模糊逻辑将对象分成簇的方法。第二部分描述了不同的模糊决策树。提出了这些树处理分类和回归问题的方式。在第三部分中，描述了森林问题-由模糊树组成的集合分类器。最后一部分讨论了在模糊森林中执行加权决策的方法。