.png){kind=icon}
返回
A boosted co-training method for class-imbalanced learning
.png){kind=icon}
PRE
AI
DOI：10.1111/exsy.13377.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Class imbalance learning (CIL) has become one of the most challenging research topics. In this article, we propose a Boosted co-training method to modify the class distribution so that traditional classifiers can be readily adapted to imbalanced datasets. This article is among the first to utilize pseudo-labelled data of co-training to enlarge the training set of minority classes. Compared with existing oversampling methods which generate minority samples based on labelled data, the proposed method has the ability to learn from unlabelled data and then decrease the risk of overfitting. Furthermore, we propose a boosting-style technique which implicitly modifies the class distribution and combines it with co-training to alleviate the bias towards majority classes. Finally, we collect two series of classifiers generated during Boosted co-training to build an ensemble for the classification. It further improves the CIL performance by leveraging the strength of ensemble learning. By taking advantage of the diversity of co-training, we also contribute a new approach to generating base classifiers for ensemble learning. The proposed method is compared with eight state-of-the-art CIL methods on a variety of benchmark datasets. Measured by G-Mean, F-Measure, and AUC, Boosted co-training achieves the best performances and average ranks on 18 benchmark datasets. The experimental results demonstrate the significant superiority of Boosted co-training over other CIL methods.
Keyword:
boosting
class-imbalanced learning
co-training
over-sampling
pseudo-labelled data
期刊
IF:
2.3
论文数:
2.6K
被引数:
3.8K
机构
引用论文
Enterprise credit risk prediction using supply chain information: A decision tree ensemble model based on the differential sampling rate, Synthetic Minority Oversampling Technique and AdaBoost.png)
EXPERT SYSTEMS
EXPERT SYSTEMSIF2.3
PREAI
分享收藏Training cost-sensitive neural networks with methods addressing the class imbalance problem用解决类不平衡问题的方法训练代价敏感的神经网络
PREAI
分享收藏GEV-NN: A deep neural network architecture for class imbalance problem in binary classification
PREAI
分享收藏Arsenic removal from aqueous solutions by adsorption using novel MIL-53(Fe) as a highly efficient adsorbent使用新型MIL-53(Fe) 作为高效吸附剂通过吸附从水溶液中去除砷RSC Advances
RSC AdvancesIF0
PREAI
分享收藏Neuronal-binding antibodies from patients with antiphospholipid syndrome induce cognitive deficits following intrathecal passive transferLupus
LupusIF0
PREAI
分享收藏Boosting with Lexicographic Programming: Addressing Class Imbalance without Cost Tuning通过词典编程进行提升: 在不调整成本的情况下解决类不平衡问题
OAAI
OAAI分享收藏
查看更多内容