Evaluation for Regression Analyses on Evolving Data Streams

2025-02-11

AI Summary

Prior research on regression for dynamic data streams suffers from insufficient methodological investigation and inconsistent evaluation practices. Method: We propose the first systematic evaluation framework tailored to streaming regression, unifying support for both point prediction and prediction interval tasks. The framework introduces a novel synthetic data generation strategy capable of precisely modeling complex concept drift types—including incremental drift—and establishes a multidimensional evaluation metric suite encompassing error measures, prediction interval coverage probability, and interval width. Contribution/Results: Extensive experiments across multiple state-of-the-art streaming regression methods demonstrate that our framework significantly enhances fairness, reproducibility, and robustness in model comparison. It provides a standardized benchmark and an extensible evaluation paradigm for streaming regression research.

Technology Category

• Machine Learning: Time-Series/Data Streams
• Data Mining & Knowledge Management: Data Stream Mining
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

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.

Problem

• Challenges of regression analysis
• Standardized evaluation process
• Innovative drift simulation strategy

Innovation

• Standardized evaluation process
• Innovative drift simulation strategy
• Comprehensive experiments validation