TY - JOUR
T1 - QuadCDD
T2 - A Quadruple-based Approach for Understanding Concept Drift in Data Streams
AU - Wang, Pingfan
AU - Yu, Hang
AU - Jin, Nanlin
AU - Davies, Duncan
AU - Woo, Wai lok
N1 - Funding information: This work was supported by the European Regional Development Fund (ERDF)[25R17P01847]; Northumbria University Newcastle, Newcastle upon Tyne, UK; Notify Technology Ltd, Newcastle, UK.
PY - 2024/3/15
Y1 - 2024/3/15
N2 - Concept drift is a prevalent phenomenon in data streams that necessitates detection and in-depth understanding, as it signifies that the statistical properties of a target variable, which the model aims to predict, change over time in unforeseen ways. Existing detection methods predominantly aim to identify the drift start time, which lack comprehensive understanding of data streams, leading to a loss of drift information. In this paper, we present a novel Quadruple-based Approach for Understanding Concept Drift in Data Streams (QuadCDD) framework that not only detects and predicts the concept drift start point but also offers a more detailed analysis of concept drift through the use of quadruples, encompassing drift start, drift end, drift severity, and drift type. Our framework employs quadruples to enable informed decision-making and adopt appropriate actions to handle various concept drifts, effectively maintaining high and stable performance in data streams with concept drift. Experimental results validate the effectiveness of our QuadCDD framework in accurately detecting and understanding concept drifts, as well as in preserving the stability and performance of models in the presence of these drifts.
AB - Concept drift is a prevalent phenomenon in data streams that necessitates detection and in-depth understanding, as it signifies that the statistical properties of a target variable, which the model aims to predict, change over time in unforeseen ways. Existing detection methods predominantly aim to identify the drift start time, which lack comprehensive understanding of data streams, leading to a loss of drift information. In this paper, we present a novel Quadruple-based Approach for Understanding Concept Drift in Data Streams (QuadCDD) framework that not only detects and predicts the concept drift start point but also offers a more detailed analysis of concept drift through the use of quadruples, encompassing drift start, drift end, drift severity, and drift type. Our framework employs quadruples to enable informed decision-making and adopt appropriate actions to handle various concept drifts, effectively maintaining high and stable performance in data streams with concept drift. Experimental results validate the effectiveness of our QuadCDD framework in accurately detecting and understanding concept drifts, as well as in preserving the stability and performance of models in the presence of these drifts.
KW - Concept drift
KW - Deep neural network
KW - Detection and understanding
KW - Performance stability
UR - http://www.scopus.com/inward/record.url?scp=85174949746&partnerID=8YFLogxK
U2 - 10.1016/j.eswa.2023.122114
DO - 10.1016/j.eswa.2023.122114
M3 - Article
SN - 0957-4174
VL - 238
JO - Expert Systems with Applications
JF - Expert Systems with Applications
M1 - 122114
ER -