CLC number: TP311
On-line Access: 2011-05-09
Received: 2010-06-18
Revision Accepted: 2010-10-08
Crosschecked: 2011-03-31
Cited: 1
Clicked: 7972
Jr-shian Chen. Extracting classification rules based on a cumulative probability distribution approach[J]. Journal of Zhejiang University Science C, 2011, 12(5): 379-386.
@article{title="Extracting classification rules based on a cumulative probability distribution approach",
author="Jr-shian Chen",
journal="Journal of Zhejiang University Science C",
volume="12",
number="5",
pages="379-386",
year="2011",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.C1000205"
}
%0 Journal Article
%T Extracting classification rules based on a cumulative probability distribution approach
%A Jr-shian Chen
%J Journal of Zhejiang University SCIENCE C
%V 12
%N 5
%P 379-386
%@ 1869-1951
%D 2011
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.C1000205
TY - JOUR
T1 - Extracting classification rules based on a cumulative probability distribution approach
A1 - Jr-shian Chen
J0 - Journal of Zhejiang University Science C
VL - 12
IS - 5
SP - 379
EP - 386
%@ 1869-1951
Y1 - 2011
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.C1000205
Abstract: This paper deals with a reinforced cumulative probability distribution approach (CPDA) based method for extracting classification rules. The method includes two phases: (1) automatic generation of the membership function, and (2) use of the corresponding linguistic data to extract classification rules. The proposed method can determine suitable interval boundaries for any given dataset based on its own characteristics, and generate the fuzzy membership functions automatically. Experimental results show that the proposed method surpasses traditional methods in accuracy.
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