Fuzzy trees and forests-Review

Zenon Antoni Sosnowski , Łukasz Gadomer

Abstract

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.
Author Zenon Antoni Sosnowski (FCS / DDMCG)
Zenon Antoni Sosnowski,,
- Department of Digital Media and Computer Graphics
, Łukasz Gadomer (FCS / DDMCG)
Łukasz Gadomer,,
- Department of Digital Media and Computer Graphics
Journal seriesWiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery, ISSN 1942-4787, (N/A 100 pkt)
Issue year2019
ASJC Classification1700 General Computer Science
DOIDOI:10.1002/widm.1316
Internal identifier000045650
Languageen angielski
Score (nominal)100
Score sourcejournalList
ScoreBUT score = 42.0, 11-07-2019, manual
Ministerial score = 100.0, 31-03-2020, ArticleFromJournal
Publication indicators Scopus SNIP (Source Normalised Impact per Paper): 2017 = 2.083; WoS Impact Factor: 2018 = 2.541 (2) - 2018=3.236 (5)
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