Toward evolving consistent, complete, and compact fuzzy rule sets for classification problems

被引：0
|
作者：
Casillas, Jorge [1 ]
Orriols-Puig, Albert [2 ]
Bernado-Mansilla, Ester [2 ]
机构：
[1] Univ Granada, Dept Comp Sci & Artificial Intelligence, E-18071 Granada, Spain
[2] Univ Ramon Llull, Grup Recerca Sistemes Intelligents, Barcelona 08022, Spain
来源：
2008 3RD INTERNATIONAL WORKSHOP ON GENETIC AND EVOLVING FUZZY SYSTEMS | 2008年
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D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
This paper proposes Pitts-DNF-C, a multiobjective Pittsburgh-style Learning Classifier System that evolves a set of DNF-type fuzzy rules for classification tasks. The system is explicitly designed to only explore solutions that lead to consistent, complete, and compact rule sets without redundancies and inconsistencies. The behavior of the system is analyzed on a collection of real-world data sets, showing its competitiveness in terms of performance and interpretability with respect to three other fuzzy learners.
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页码：87 / +
页数：2
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