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C-fuzzy variable-branch decision tree with storage and classification error rate constraints

[+] Author Affiliations
Shiueng-Bien Yang

Wenzao Ursuline College of Languages, Department of Information Management and Communication, 900 Mintsu 1st Road, Kaohsing 807, Taiwan

J. Electron. Imaging. 18(4), 043013 (December 21, 2009). doi:10.1117/1.3274613
History: Received November 09, 2008; Revised October 12, 2009; Accepted October 28, 2009; Published December 21, 2009; Online December 21, 2009
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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.

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Topics

Data storage

Citation

Shiueng-Bien Yang
"C-fuzzy variable-branch decision tree with storage and classification error rate constraints", J. Electron. Imaging. 18(4), 043013 (December 21, 2009). ; http://dx.doi.org/10.1117/1.3274613


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