An Efficient Rule-based Classification of Diabetes Using ID3, C4.5 & CART Ensembles

被引：30

作者：

Bashir, Saba ^{[1
]}

Qamar, Usman ^{[1
]}

Khan, Farhan Hassan ^{[1
]}

Javed, M. Younus ^{[1
]}

机构：

[1] Natl Univ Sci & Technol, Coll Elect & Mech Engn, Islamabad, Pakistan

来源：

PROCEEDINGS OF 2014 12TH INTERNATIONAL CONFERENCE ON FRONTIERS OF INFORMATION TECHNOLOGY | 2014年

关键词：

Diabetes; Bagging; Boosting; Adaboost; Bayesian boosting; Stacking; Ensemble Classifiers; Decision trees;

D O I：

10.1109/FIT.2014.50

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

Conventional techniques for clinical decision support systems are based on a single classifier or simple combination of these classifiers used for disease diagnosis and prediction. Recently much attention has been paid on improving the performance of disease prediction by using ensemble-based methods. In this paper, we use multiple ensemble classification techniques for diabetes datasets. Three types of decision trees ID3, C4.5 and CART are used as the base classifiers. The ensemble techniques used are Majority Voting, Adaboost, Bayesian Boosting, Stacking and Bagging. Two benchmark diabetes datasets are used from UCI and BioStat repositories respectively. Experimental results and evaluation show that Bagging ensemble technique shows better performance as compared to single as well as other ensemble techniques.

引用

页码：226 / 231

页数：6