Fast concept drift detection using unlabeled data

被引：0
|
作者：
Shang, Dan [1 ]
Zhang, Guangquan [1 ]
Lu, Jie [1 ]
机构：
[1] Univ Technol Sydney, Ctr Artificial Intelligence, Fac Engn & Informat Technol, Sydney, NSW 2007, Australia
来源：
DEVELOPMENTS OF ARTIFICIAL INTELLIGENCE TECHNOLOGIES IN COMPUTATION AND ROBOTICS | 2020年 / 12卷
基金：
澳大利亚研究理事会;
关键词：
Concept Drift; Unsupervised Learning; Stream Data Mining;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Streaming data mining is in use today in many industrial applications, but performance of the models is deteriorated by concept drift, especially when true labels are unavailable. This paper addresses the need of detecting concept drifts under unsupervised situation and proposes the Unsupervised Concept Drift Detection (UCDD) method. A cluster technique is first applied to determine artificial labels of the data set, then a fast drift detection algorithm is used to detect the boundary change between the labeled clusters. Through the empirical evaluation, the method demonstrates effectiveness on detecting various types of concept drifts.
引用
收藏
页码：133 / 140
页数：8
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