Accuracy vs. Interpretability of Fuzzy Rule-Based Classifiers: An Evolutionary Approach

被引：0
|
作者：
Gorzalczany, Marian B. [1 ]
Rudzinski, Filip [1 ]
机构：
[1] Kielce Univ Technol, Dept Elect & Comp Engn, PL-25314 Kielce, Poland
来源：
SWARM AND EVOLUTIONARY COMPUTATION | 2012年 / 7269卷
关键词：
SYSTEMS; DESIGN;
D O I：
暂无
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
The paper presents a generalization of the Pittsburgh approach to learn fuzzy classification rules from data. The proposed approach allows us to obtain a fuzzy rule-based system with a predefined level of compromise between its accuracy and interpretability (transparency). The application of the proposed technique to design the fuzzy rule-based classifier for the well known benchmark data sets (Dermatology and Wine) available from the http://archive.ics.uci.edu/ml is presented. A comparative analysis with several alternative (fuzzy) rule-based classification techniques has also been carried out.
引用
收藏
页码：222 / 230
页数：9
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