Data Mining by Symbolic Fuzzy Classifiers and Genetic Programming

被引：0
|
作者：
Owais, Suhail [1 ]
Kroemer, Pavel [2 ]
Platos, Jan [2 ]
Snasel, Vaclav [2 ]
Zelinka, Ivan [2 ]
机构：
[1] ASU, Dept Comp Sci, IT Coll, Amman, Jordan
[2] VSB Tech Univ Ostrava, Dept Comp Sci, FEECS, Ostrava, Czech Republic
来源：
NOSTRADAMUS: MODERN METHODS OF PREDICTION, MODELING AND ANALYSIS OF NONLINEAR SYSTEMS | 2013年 / 192卷
关键词：
TEXT RETRIEVAL;
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：
There are various techniques for data mining and data analysis. Among them, hybrid approaches combining two or more algorithms gain importance as the complexity and dimension of real world data sets grows. In this paper, we present an application of evolutionary-fuzzy classification technique for data mining. Genetic programming is deployed to evolve a fuzzy classifier and an example of real world application is presented.
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页码：273 / +
页数：3
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