%0 Conference Proceedings
%T Using Cluster–Context Fuzzy Decision Trees in Fuzzy Random Forest
%+ Białystok University of Technology
%A Gadomer, Łukasz
%A Sosnowski, Zenon, A.
%Z Part 3: Data Analysis and Information Retrieval
%< avec comité de lecture
%( Lecture Notes in Computer Science
%B 16th IFIP International Conference on Computer Information Systems and Industrial Management (CISIM)
%C Bialystok, Poland
%Y Khalid Saeed
%Y Władysław Homenda
%Y Rituparna Chaki
%I Springer International Publishing
%3 Computer Information Systems and Industrial Management
%V LNCS-10244
%P 180-192
%8 2017-06-16
%D 2017
%R 10.1007/978-3-319-59105-6_16
%K Context–Cluster Fuzzy Decision Tree
%K C–Fuzzy Decision Tree
%K Context–Based Fuzzy Clustering
%K Fuzzy Random Forest
%Z Computer Science [cs]
%Z Humanities and Social Sciences/Library and information sciencesConference papers
%X Cluster–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.
%G English
%Z TC 8
%2 https://inria.hal.science/hal-01656242/document
%2 https://inria.hal.science/hal-01656242/file/448933_1_En_16_Chapter.pdf
%L hal-01656242
%U https://inria.hal.science/hal-01656242
%~ SHS
%~ IFIP-LNCS
%~ IFIP
%~ IFIP-TC
%~ IFIP-TC8
%~ IFIP-CISIM
%~ IFIP-LNCS-10244