TY - GEN
T1 - RealDriftGenerator
T2 - 2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024
AU - Lin, Borong
AU - Huang, Chao
AU - Zhu, Xiaohui
AU - Jin, Nanlin
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - Concept drift refers to the probability distribution of data generation changes over time in a data stream environment. In recent years, there has been an increasing interest in drift detection models. However, due to the lack of labeled concept drift datasets, most researchers tend to using synthetic drift data generators for model training. These generators only have relatively simple feature distributions, which fail to capture the complexity found in real-world scenarios. This paper introduces a real scenario concept drift label generator (RealDriftGenerator). This generator aims to preserve the complexity and temporal correlation of real-world scenario while generating concept drifts with user defined drift positions and drift widths. The validation result show that the temporal correlation coefficients of RealDriftGenerator is significantly higher than benchmark drift generators. Additionally, the ability of RealDriftGenerator to capture the complexity in real-world scenarios is 20% higher than benchmark drift generators(measured by model performance). The source code of RealDriftGenerator has been published on https://github.com/sniperrifle71/realDriftGenerator.
AB - Concept drift refers to the probability distribution of data generation changes over time in a data stream environment. In recent years, there has been an increasing interest in drift detection models. However, due to the lack of labeled concept drift datasets, most researchers tend to using synthetic drift data generators for model training. These generators only have relatively simple feature distributions, which fail to capture the complexity found in real-world scenarios. This paper introduces a real scenario concept drift label generator (RealDriftGenerator). This generator aims to preserve the complexity and temporal correlation of real-world scenario while generating concept drifts with user defined drift positions and drift widths. The validation result show that the temporal correlation coefficients of RealDriftGenerator is significantly higher than benchmark drift generators. Additionally, the ability of RealDriftGenerator to capture the complexity in real-world scenarios is 20% higher than benchmark drift generators(measured by model performance). The source code of RealDriftGenerator has been published on https://github.com/sniperrifle71/realDriftGenerator.
UR - http://www.scopus.com/inward/record.url?scp=85217846283&partnerID=8YFLogxK
U2 - 10.1109/SMC54092.2024.10831569
DO - 10.1109/SMC54092.2024.10831569
M3 - Conference Proceeding
AN - SCOPUS:85217846283
T3 - Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
SP - 1124
EP - 1129
BT - 2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 6 October 2024 through 10 October 2024
ER -