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WIREs Data Mining Knowl Discov
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Fuzzy trees and forests—Review

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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. This article is categorized under: Fundamental Concepts of Data and Knowledge > Big Data Mining Technologies > Classification Technologies > Prediction Technologies > Machine Learning
Example division of decision attribute into five contexts using (from left) triangular, Gaussian and trapezoidal membership functions
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Example division of decision attribute into four contexts using Gaussian function with context configuration value (from left) 0.6 and 1.6
[ Normal View | Magnified View ]

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Technologies > Prediction
Fundamental Concepts of Data and Knowledge > Big Data Mining
Technologies > Machine Learning
Technologies > Classification

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