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Journal of Electronic Imaging

C-fuzzy variable-branch decision tree with storage and classification error rate constraints
Author(s): Shiueng-Bien Yang
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Paper Abstract

The C-fuzzy decision tree (CFDT), which is based on the fuzzy C-means algorithm, has recently been proposed. The CFDT is grown by selecting the nodes to be split according to its classification error rate. However, the CFDT design does not consider the classification time taken to classify the input vector. Thus, the CFDT can be improved. We propose a new C-fuzzy variable-branch decision tree (CFVBDT) with storage and classification error rate constraints. The design of the CFVBDT consists of two phases-growing and pruning. The CFVBDT is grown by selecting the nodes to be split according to the classification error rate and the classification time in the decision tree. Additionally, the pruning method selects the nodes to prune based on the storage requirement and the classification time of the CFVBDT. Furthermore, the number of branches of each internal node is variable in the CFVBDT. Experimental results indicate that the proposed CFVBDT outperforms the CFDT and other methods.

Paper Details

Date Published: 1 October 2009
PDF: 10 pages
J. Electron. Imaging. 18(4) 043013 doi: 10.1117/1.3274613
Published in: Journal of Electronic Imaging Volume 18, Issue 4
Show Author Affiliations
Shiueng-Bien Yang, Wenzao Ursuline College of Languages (Taiwan)


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