C-fuzzy random forests with unpruned trees and trees constructed using each of these pruning methods were created. Five pruning methods were adjusted to mentioned kind of trees and examined: Reduced Error Pruning (REP), Pessimistic Error Pruning (PEP), Minimum Error Pruning (MEP), Critical Value Pruning (CVP) and Cost-Complexity Pruning. This solution is based on fuzzy random forest and uses C-fuzzy decision trees or Cluster–context fuzzy decision trees-depending on the variant. C-fuzzy random forest is a classifier which we created and we are improving. In this paper, the idea of applying different pruning methods to C-fuzzy decision trees and Cluster–context fuzzy decision trees in C-fuzzy random forest is presented. Pruning decision trees is the way to decrease their size in order to reduce classification time and improve (or at least maintain) classification accuracy.
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