Credal Decision Trees to Classify Noisy Data Sets

被引：0
|
作者：
Mantas, Carlos J. [1 ]
Abellan, Joaquin [1 ]
机构：
[1] Univ Granada, Dept Comp Sci & Artificial Intelligence, Granada, Spain
来源：
HYBRID ARTIFICIAL INTELLIGENCE SYSTEMS, HAIS 2014 | 2014年 / 8480卷
关键词：
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：
Credal Decision Trees (CDTs) are algorithms to design classifiers based on imprecise probabilities and uncertainty measures. C4.5 and CDT procedures are combined in this paper. The new algorithm builds trees for solving classification problems assuming that the training set is not fully reliable. This algorithm is especially suitable to classify noisy data sets. This is shown in the experiments.
引用
收藏
页码：689 / 696
页数：8
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