The idea of knowledge aggregation contained in C-fuzzy decision tree nodes with OWA operators during the C-fuzzy random forest decision-making process is presented in this paper. C-fuzzy random forest is a new kind of ensemble classifier which consists of C-fuzzy decision trees. There are proposed three kinds of OWA operators for the given problem, called Local OWA, global OWA for each tree in the forest and global OWA for the whole forest. Weights of OWA operators are optimized using a genetic algorithm. In order to evaluate the created classifier, experiments were performed using ten datasets. The classifier was checked in comparison with C4.5 rev. 8 decision tree and single C-fuzzy decision tree. The influence of randomness and proposed OWA operators on the classification accuracy was tested.

Part 7: DecisionsInternational audienceIn this paper a new classification solution which joins C–Fuzzy Decision Trees and Fuzzy Random Forest is proposed. Its assumptions are similar to the Fuzzy Random Forest, but instead of fuzzy trees it consists of C–Fuzzy Decision Trees. To test the proposed classifier there was performed a set of experiments. These experiments were performed using four datasets: Ionosphere, Dermatology, Pima–Diabetes and Hepatitis. Created forest was compared to C4.5 rev. 8 Decision Tree and single C–Fuzzy Decision Tree. The influence of randomness on the classification accuracy was also tested

Part 3: Data Analysis and Information RetrievalInternational audienceCluster–Context Fuzzy Decision Tree is the classifier which joins C–Fuzzy Decision Tree with Context–Based Fuzzy Clustering method. The idea of using this kind of tree in the Fuzzy Random Forest is presented in this paper. The created ensemble classifier has similar assumptions to the Fuzzy Random Forest, but differs in the kind of used trees and all aspects connected with this difference. The quality of the created classifier was evaluated by several experiments performed on different datasets. There were tested both datasets with discrete and continuous attributes and decision classes. The aspect of using a randomness in the created classifier was also evaluated

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

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