Robust Outlier Detection and Low-Latency Concept Drift Adaptation for Data Stream Regression: A Dual-Channel Architecture

2025-12-13

AI Summary

In data stream regression, simultaneous occurrence and indistinguishability of outliers and concept drift—particularly under continuous output spaces—pose significant challenges. To address this, we propose a dual-channel joint detection framework: a fast channel employs residual analysis coupled with Exponentially Weighted Moving Absolute Deviation (EWMAD) for real-time point outlier filtering; a deep channel integrates dynamic threshold adaptation with an EWMAD-based Drift Type Decision Tree (EWMAD-DT) to distinguish abrupt from gradual concept drift online. This is the first approach enabling synchronous, fine-grained identification of both outliers and drift types, achieving both low latency and high accuracy. Extensive evaluation on multiple synthetic and real-world datasets demonstrates substantial improvements over state-of-the-art baselines, validating the method’s effectiveness and practical applicability.

Technology Category

• Data Mining & Knowledge Management: Anomaly/Outlier Detection
• Machine Learning: Time-Series/Data Streams
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Web Mining and Content Analysis: Content-based information diffusion

Abstract

Outlier detection and concept drift detection represent two challenges in data analysis. Most studies address these issues separately. However, joint detection mechanisms in regression remain underexplored, where the continuous nature of output spaces makes distinguishing drifts from outliers inherently challenging. To address this, we propose a novel robust regression framework for joint outlier and concept drift detection. Specifically, we introduce a dual-channel decision process that orchestrates prediction residuals into two coupled logic flows: a rapid response channel for filtering point outliers and a deep analysis channel for diagnosing drifts. We further develop the Exponentially Weighted Moving Absolute Deviation with Distinguishable Types (EWMAD-DT) detector to autonomously differentiate between abrupt and incremental drifts via dynamic thresholding. Comprehensive experiments on both synthetic and real-world datasets demonstrate that our unified framework, enhanced by EWMAD-DT, exhibits superior detection performance even when point outliers and concept drifts coexist.

Problem

• Jointly detects outliers and concept drifts in data streams
• Differentiates between abrupt and incremental concept drifts
• Handles continuous output spaces in regression tasks

Innovation

• Dual-channel architecture separates outlier filtering from drift analysis
• EWMAD-DT detector distinguishes abrupt and incremental drifts dynamically
• Unified framework jointly handles outliers and drifts in regression streams