EVALUATION OF ROBUSTNESS OF ENSEMBLE LEARNERS TO NOISY DATA

被引：0

作者：

Albayrak, Abdulkadir ^{[1
]}

Cingiz, M. Ozgur ^{[1
]}

Amasyali, M. Fatih ^{[1
]}

机构：

[1] Yildiz Tekn Univ, Bilgisayar Muhendisligi Bolumu, Istanbul, Turkey

来源：

2013 21ST SIGNAL PROCESSING AND COMMUNICATIONS APPLICATIONS CONFERENCE (SIU) | 2013年

关键词：

Noisy Data; Ensemble Methods; Classification;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Discovering noisy data and classification of noisy data sets are problematic issues associated with noisy data sets. In our work, we used 36 UCI data sets that consist of differeent rates of noisy data to measure robustness of five ensemble learners and two basic classifiers to noisy data. According to classification success ratesof our study, Random Subspace and Bagging are more robust to noisy data than other ensemble learners and simple classifiers.

引用

页数：4

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