Evaluation for Regression Analyses on Evolving Data Streams
This work addresses the problem of regression analysis in evolving data streams, which is significant for researchers and practitioners dealing with streaming data, and offers an incremental yet valuable contribution to the field.
The paper tackles the challenge of regression analysis in evolving data streams and proposes a standardized evaluation process, achieving robust results with state-of-the-art methods. The approach also introduces a drift simulation strategy for various drift types.
The paper explores the challenges of regression analysis in evolving data streams, an area that remains relatively underexplored compared to classification. We propose a standardized evaluation process for regression and prediction interval tasks in streaming contexts. Additionally, we introduce an innovative drift simulation strategy capable of synthesizing various drift types, including the less-studied incremental drift. Comprehensive experiments with state-of-the-art methods, conducted under the proposed process, validate the effectiveness and robustness of our approach.