Fuzzy trees and forests-Review
Zenon Antoni Sosnowski , Łukasz Gadomer
AbstractData 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.
|Journal series||Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery, ISSN 1942-4787, (N/A 100 pkt)|
|Score|| = 42.0, 11-07-2019, manual|
= 100.0, 31-03-2020, ArticleFromJournal
|Publication indicators||: 2017 = 2.083; : 2018 = 2.541 (2) - 2018=3.236 (5)|
* presented citation count is obtained through Internet information analysis and it is close to the number calculated by the Publish or Perish system.