Features Selection in Character Recognition with Random Forest Classifier

被引：0

作者：

Homenda, Wladyslaw ^{[1
,2
]}

Lesinski, Wojciech ^{[2
]}

机构：

[1] Warsaw Univ Technol, Fac Math & Informat Sci, Plac Politech 1, PL-00660 Warsaw, Poland

[2] Univ Bialystok, Fac Math & Comp Sci, Bialystok, Poland

来源：

COMPUTATIONAL COLLECTIVE INTELLIGENCE: TECHNOLOGIES AND APPLICATIONS, PT I | 2011年 / 6922卷

关键词：

pattern recognition; features selection; classification; random forest;

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：

Proper image recognition depends on many factors. Features' selection and classifiers are most important ones. In tins paper we discuss a number of features and several classifiers. The study is focused on how features' selection affects classifier efficiency with special attention given to random forests. Different construction methods of decision trees are considered. Others classifiers (k nearest neighbors, decision trees and classifier with Mahalanobis distance) were used for efficiency comparison. Lower case letters from Latin alphabet are used in empirical tests of recognition efficiency.

引用

页码：93 / +

页数：2

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