Mining fuzzy rules using an Artificial Immune System with fuzzy partition learning

被引：22
作者：
Mezyk, Edward [1 ]
Unold, Olgierd [1 ]
机构：
[1] Wroclaw Univ Technol, Inst Comp Engn Control & Robot, PL-50370 Wroclaw, Poland
来源：
APPLIED SOFT COMPUTING | 2011年 / 11卷 / 02期
关键词：
Machine learning; Fuzzy logic; Artificial Immune System; Data mining; CLASSIFICATION RULES; CLASSIFIERS; ALGORITHMS; SELECTION; AIRS;
D O I：
10.1016/j.asoc.2010.06.012
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
The paper introduces accuracy boosting extension to a novel induction of fuzzy rules from raw data using Artificial Immune System methods. Accuracy boosting relies on fuzzy partition learning. The performance, in terms of classification accuracy, of the proposed approach was compared with traditional classifier schemes: C4.5, Naive Bayes, K*, Meta END, JRip, and Hyper Pipes. The result accuracy of these methods are significantly lower than accuracy of fuzzy rules obtained by method presented in this study (paired t-test, P < 0.05). (C) 2010 Elsevier B.V. All rights reserved.
引用
收藏
页码：1965 / 1974
页数：10
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