Transformation-Based Hierarchical Decision Rules using Genetic Algorithms and its Application to Handwriting Recognition Domain

被引：0

作者：

Su, Tonghua ^{[1
]}

Zhang, Tianwen ^{[1
]}

Huang, Hujie ^{[1
]}

Xue, Guixiang ^{[2
]}

Zhao, Zheng ^{[2
]}

机构：

[1] Harbin Inst Technol, Sch Comp Sci & Technol, Harbin 150001, Peoples R China

[2] Tianjin Univ, Sch Comp Sci & Technol, Tianjin 300072, Peoples R China

来源：

2008 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION, VOLS 1-8 | 2008年

基金：

美国国家科学基金会; 中国国家自然科学基金;

关键词：

D O I：

10.1109/CEC.2008.4630911

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 describes a new approach based on Transformation-Based Learning for extracting hierarchical decision rules. Genetic algorithms are adapted to establish the context environment for transformation operation and the transformation operation can lengthen the life cycle of "good" candidate rules. The experiments are conducted on iris, wine and glass datasets with a 10-fold cross validation setup. The results show that transformation operation can improve the precision of the classifier with a smaller number of rules and generations than hierarchical decision rules. The approach also works well in touching block extraction of Chinese handwritten text.

引用

页码：951 / +

页数：2

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