.png){kind=icon}
Return
Detecting group concept drift from multiple data streams
.png){kind=icon}
PRE
AI
DOI：10.1016/j.patcog.2022.109113.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Concept drift may lead to a sharp downturn in the performance of streaming in data-based algorithms, caused by unforeseeable changes in the underlying distribution of data. In this paper, we are mainly concerned with concept drift across multiple data streams, and in situations where the drift of each data stream cannot be detected in time, due to slight underlying distribution drifts. We call this group concept drift. When compared to the detection of concept drift for a single data stream, the challenges of detecting group concept drift arise from three aspects: first, the training data become more complex; second, the underlying distribution becomes more complex; and third, the correlations between data streams become more complex. To address these challenges, the key idea of our method is to construct a distribution free test statistic, free from any underlying distribution in multiple data streams. Then, for streaming data, we design an online learning algorithm to obtain this test statistic, thereby determining the concept drift caused by the hypothesis test. The experiment evaluations with both synthetic and realworld datasets prove that our method can accurately detect concept drift from multiple data streams.(c) 2022 Elsevier Ltd. All rights reserved.
Keywords:
Concept drift
Data streams
Online learning
Hypothesis test
Journal
IF:
7.6
Papers:
1.3W
Citations:
4.5W
Organization
View more organizations
Cited Papers
ShareSaveOnline Topology Learning by a Gaussian Membership-Based Self-Organizing Incremental Neural Network
PREAI
ShareSave
ShareSaveShareSaveElastic gradient boosting decision tree with adaptive iterations for concept drift adaptation.png)
NEUROCOMPUTING
NEUROCOMPUTINGIF6.5
PREAI
ShareSaveShareSave View more