.png){kind=icon}
返回
Selective ensemble method for anomaly detection based on parallel learning
.png){kind=icon}
OA
AI
DOI：10.1038/s41598-024-51849-3.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Anomaly detection is a highly important task in the field of data analysis. Traditional anomaly detection approaches often strongly depend on data size, structure and features, while introducing the idea of ensemble into anomaly detection can greatly improve the generalization ability. Ensemble-based anomaly detection methods still face some challenges, however, such as data imbalance, time and space demand and the selection of base detectors. To this end, we propose a selective ensemble method for anomaly detection based on parallel learning (SEAD-PL). First, a differentiated stratified sampling method is designed to alleviate the problem of data imbalance. Then, a distributed parallel training frame is built to address the problem of excessive time and space consumption for base detector training. Finally, a clustering-based ensemble selection strategy is introduced to balance the accuracy and diversity of base detectors. Experiments are performed on six datasets, which demonstrate that the proposed method has obvious advantages over four selected methods.
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3.9
论文数:
28.3W
被引数:
83.5W
机构
引用论文
.png){kind=icon}
分享收藏Building a Bridge of Bounding Box Regression Between Oriented and Horizontal Object Detection in Remote Sensing Images在遥感图像中建立定向和水平目标检测之间的边界盒回归桥梁
PREAI
分享收藏A Comparative Study on PFC Bridgeless Flyback and SEPIC AC-DC Rectifiers Operating in DCM and BCM
PREAI
分享收藏
查看更多内容