Abstract
In 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.
Author supplied keywords
Cite
CITATION STYLE
Gadomer, Ł., & Sosnowski, Z. A. (2016). Fuzzy random forest with c-fuzzy decision trees. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9842 LNCS, pp. 481–492). Springer Verlag. https://doi.org/10.1007/978-3-319-45378-1_43
Register to see more suggestions
Mendeley helps you to discover research relevant for your work.
