Algorithm recommendation for data streams

In the last decades, many companies have taken advantage of knowledge discovery to identify valuable information in massive volumes of data generated at high frequency. Machine learning techniques can be employed for knowledge discovery since they can extract patterns from data and induce models to predict future events. However, dynamic and evolving environments usually generate non-stationary data streams. Hence, models trained in these scenarios may perish over time due to seasonality or concept drift. Periodic retraining can help, but a fixed hypothesis space may no longer be appropriate. An alternative solution is to use meta-learning for regular algorithm selection in time-changing environments, choosing the bias that best suits the current data. In this paper, we present an enhanced framework for data stream algorithm selection based on MetaStream. Our approach uses meta-learning and incremental learning to actively select the best algorithm for the current concept in a time-changing environment. Different from previous work, we use a rich set of state-of-the-art meta-features, and an incremental learning approach in the meta-level based on LightGBM. The results show that this new strategy can improve the recommendation accuracy of the best algorithm in time-changing data.