IEEE Trans Neural Netw Learn Syst. 2020 Jan;31(1):309-320. doi: 10.1109/TNNLS.2019.2900956. Epub 2019 Mar 26.
We present a novel method for concept drift detection, based on: 1) the development and continuous updating of online sequential extreme learning machines (OS-ELMs) and 2) the quantification of how much the updated models are modified by the newly collected data. The proposed method is verified on two synthetic case studies regarding different types of concept drift and is applied to two public real-world data sets and a real problem of predicting energy production from a wind plant. The results show the superiority of the proposed method with respect to alternative state-of-the-art concept drift detection methods. Furthermore, updating the prediction model when the concept drift has been detected is shown to allow improving the overall accuracy of the energy prediction model and, at the same time, minimizing the number of model updatings.
1）在线序贯极端学习机（OS-ELM）的开发和持续更新，以及 2）量化新收集的数据对更新模型的修改程度。所提出的方法在两个关于不同类型概念漂移的合成案例研究中得到验证，并应用于两个公共的真实数据集和一个预测风电场能源产量的实际问题。结果表明，与其他先进的概念漂移检测方法相比，该方法具有优越性。此外，当检测到概念漂移时更新预测模型被证明可以提高能源预测模型的整体准确性，同时最小化模型更新的次数。
