Jan 31, 2018 · In this paper we would like to present the method of using OWA operators in C-fuzzy random forest classification process. These operators ...

The classification accuracy using C-fuzzy random forest with OWA operators is com- pared with C4.5 decision tree and C-fuzzy decision trees working singly. The ...

Weights of OWA operators are optimized using a genetic algorithm. In order to evaluate the created classifier, experiments were performed using ten datasets.

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 ...

Abstract: The idea of knowledge aggregation contained in C-fuzzy decision tree nodes with OWA operators during the C-fuzzy random forest decision-making ...

Jan 31, 2018 · 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 ...

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 ...

2016. Knowledge aggregation in decision-making process with C-fuzzy random forest using OWA operators. Ł Gadomer, ZA Sosnowski. Soft Computing 23, 3741-3755 ...

Knowledge aggregation in decision-making process with C-fuzzy random forest using OWA operators · Lukasz GadomerZ. A. Sosnowski. Computer Science, Mathematics.

This paper proposes a new assignment algorithm by using the OWA operator and different extensions of it in the Branch-and-bound algorithm.