A new measure for comparing stopping criteria of fuzzy decision tree
Mohsen Zeinalkhani, Mahdi Eftekhari · 2011
Fuzzy decision trees (FDT) successfully merged approximate reasoning offered by fuzzy representation and decision trees, while preserving advantages of both: uncertainty handling and gradual processing of the former with the comprehensibility, popularity and ease of application of the latter. Size and accuracy of FDT can be controlled by stopping criteria. Comparing stopping criteria based on accuracy of generated FDTs is the simplest comparison method which doesn't consider all aspects of them. In this paper, a new measure, named Growth Control Capability (GCC), for comparing stopping criteria is introduced which determines its ability to control the number of node expansions by changing its threshold value. Different stopping criteria are used for FDT induction and are compared based on proposed measure. The obtained results show that the number of instances stopping criterion can control FDT growth better than the other ones. Therefore, one can use this stopping criterion in order to produce an FDT with predefined number of nodes.