Using Cluster–Context Fuzzy Decision Trees in Fuzzy Random Forest

Abstract : 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.
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Khalid Saeed; Władysław Homenda; Rituparna Chaki. 16th IFIP International Conference on Computer Information Systems and Industrial Management (CISIM), Jun 2017, Bialystok, Poland. Springer International Publishing, Lecture Notes in Computer Science, LNCS-10244, pp.180-192, 2017, Computer Information Systems and Industrial Management. 〈10.1007/978-3-319-59105-6_16〉
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Łukasz Gadomer, Zenon Sosnowski. Using Cluster–Context Fuzzy Decision Trees in Fuzzy Random Forest. Khalid Saeed; Władysław Homenda; Rituparna Chaki. 16th IFIP International Conference on Computer Information Systems and Industrial Management (CISIM), Jun 2017, Bialystok, Poland. Springer International Publishing, Lecture Notes in Computer Science, LNCS-10244, pp.180-192, 2017, Computer Information Systems and Industrial Management. 〈10.1007/978-3-319-59105-6_16〉. 〈hal-01656242〉

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