A Multi-classifier System Using Mean Field Genetic Algorithm

被引：0
|
作者：
Kim, Yeongjoon [1 ]
Hong, Chuleui [1 ]
机构：
[1] Sangmyung Univ, Dept Comp Sci, Seoul, South Korea
来源：
CONVERGENCE AND HYBRID INFORMATION TECHNOLOGY | 2012年 / 310卷
关键词：
Multiple-classifier; Hybrid algorithms; inductive learning;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
This paper presents an approach for building a multi-classifier system in Mean Field Genetic Algorithm (MGA) based inductive learning environments. Several base classifiers are combined with a meta-classifier that learns the bias of base classifiers so that it can draw a decision by combining predictions made by base classifiers. MGA is a hybrid algorithm of Mean Field Annealing (MFA) and Simulated annealing-like Genetic Algorithm (SGA). The proposed MGA combines the benefit of rapid convergence property of MFA and the effective genetic operations of SGA.
引用
收藏
页码：121 / 128
页数：8
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