Efficient Data Selection Indicators for Updating Models under Data Drifted Environment
Yuma Konno, Miyuki Nakano, Masato Oguchi · 2022 IEEE International Conference on Big Data (Big Data) · 2022
The long-term use of machine learning models can result in degraded performance due to data drift and other factors. We have previously proposed a data selection mechanism for time-series data of machine learning models. When data drift occurs, the models have to learn again using large-scale stream data. Thus, it is important for machine learning algorithms to introduce a mechanism avoiding useless data. This study examines the effect of data selection with an adversarial classifier using synthetic data.