MULTIPLE BINARY DECISION TREE CLASSIFIERS

被引：29
|
作者：
SHLIEN, S
机构：
[1] Communications Research Centre, Nepean, K2H 8S2
来源：
PATTERN RECOGNITION | 1990年 / 23卷 / 07期
关键词：
Character recognition; Dempster-Shafer decision theory; Minimum entropy; Misclassification error; Pattern recognition; Tree classifiers;
D O I：
10.1016/0031-3203(90)90098-6
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Binary decision trees based on nonparametric statistical models of the data provide a solution to difficult decision problems where there are many classes and many available features related in a complex manner. Unfortunately, the technique requires a very large training set and is often limited by the size of the training set rather than by the discriminatory power of the features. This paper demonstrates that higher classification accuracies can be obtained from the same training set by using a combination of decision trees and by reaching a consensus using Dempster and Shafer's theory of evidence. © 1990.
引用
收藏
页码：757 / 763
页数：7
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