ENSEMBLE CLASSIFIER AND RESAMPLING FOR IMBALANCED MULTICLASS LEARNING

被引：0
|
作者：
Sainin, Mohd Shamrie [1 ]
Ahmad, Faudziah [1 ]
Alfred, Rayner [2 ]
机构：
[1] Univ Utara Malaysia, Sch Comp, Kedah, Malaysia
[2] Univ Malaysia Sabah, Fac Comp & Informat, Sabah, Malaysia
来源：
PROCEEDINGS OF THE 5TH INTERNATIONAL CONFERENCE ON COMPUTING & INFORMATICS | 2015年
关键词：
ensemble classifier; DECIML; imbalance; multiclass; data mining; machine learning; sampling;
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：
An ensemble classifier called DECIML has previously reported that the classifier is able to perform on benchmark data compared to several single classifiers and ensemble classifiers such as AdaBoost, Bagging and Random Forest. The implementation of the ensemble using sampling was carried out in order to investigate if there are any improvements in the classification performances of the DECIML. Random sampling with replacement (SWR) method is applied to minority class in the imbalanced multiclass data. Results show that the SWR is able to increase the average performance of the ensemble classifier.
引用
收藏
页码：751 / 756
页数：6
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