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

TL;DR

This paper introduces a dual-channel regression framework that jointly detects outliers and concept drifts in data streams, employing a novel detector to differentiate drift types, thereby improving detection accuracy in complex scenarios.

Contribution

It presents a novel joint detection framework with a dual-channel architecture and a new detector for differentiating drift types, addressing a gap in existing separate detection methods.

Findings

Superior detection performance on synthetic datasets

Effective differentiation between abrupt and incremental drifts

Robust handling of coexisting outliers and drifts

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.