Feature subset selection using genetic algorithms for handwritten digit recognition

被引：30

作者：

Oliveira, LS ^{[1
]}

Benahmed, N ^{[1
]}

Sabourin, R ^{[1
]}

Bortolozzi, F ^{[1
]}

Suen, CY ^{[1
]}

机构：

[1] PUCPR, PPGIA, LARDOC, BR-80215901 Curitiba, Parana, Brazil

来源：

XIV BRAZILIAN SYMPOSIUM ON COMPUTER GRAPHICS AND IMAGE PROCESSING, PROCEEDINGS | 2001年

关键词：

D O I：

10.1109/SIBGRAPI.2001.963077

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper two approaches of genetic algorithm for feature subset selection are compared. The first approach considers a simple genetic algorithm (SGA) while the second one takes into account an iterative genetic algorithm (IGA) which is claimed to converge faster than SGA. Initially, we present an overview of the system to be optimized and the methodology applied in the experiments as well. Afterwards we discuss the advantages and drawbacks of each approach based on the experiments carried out on NIST SD19. Finally, we conclude that the IGA converges faster than the SGA, however, the SGA seems more suitable for our problem.

引用

页码：362 / 369

页数：2

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