Memetic Feature Selection: Benchmarking Hybridization Schemata

被引：0
|
作者：
Esseghir, M. A. [1 ,2 ,3 ]
Goncalves, Gilles [1 ,2 ]
Slimani, Yahya [3 ]
机构：
[1] Univ Lille Nord de France, F-59000 Lille, France
[2] Artois Univ, LGI2A Lab, F-62400 Arras, France
[3] Tunis El Manar Univ, Fac Sci Tunis, Tunis 1060, Tunisia
来源：
HYBRID ARTIFICIAL INTELLIGENCE SYSTEMS, PT 1 | 2010年 / 6076卷
关键词：
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：
Feature subset selection is an important preprocessing and guiding step for classification. The combinatorial nature of the problem have made the use of evolutionary and heuristic methods indispensble for the exploration of high dimensional problem search spaces. In this paper, a set of hybridization schemata of genetic algorithm with local search are investigated through a memetic framework. Empirical study compares and discusses the effectiveness of the proposed local search procedure as well as their components.
引用
收藏
页码：351 / +
页数：2
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