OLC-WA: Drift Aware Tuning-Free Online Classification with Weighted Average

TL;DR

OLC-WA is an adaptive, hyperparameter-free online classification model that automatically detects and adjusts to concept drift, maintaining high accuracy in evolving data streams without manual tuning.

Contribution

This paper introduces OLC-WA, a novel online classification method that dynamically detects concept drift and adapts without requiring hyperparameter tuning.

Findings

Achieves comparable accuracy to batch models in stationary environments.

Outperforms leading online baselines by 10-25% under concept drift.

Effectively adapts to evolving data distributions in streaming environments.

Abstract

Real-world data sets often exhibit temporal dynamics characterized by evolving data distributions. Disregarding this phenomenon, commonly referred to as concept drift, can significantly diminish a model's predictive accuracy. Furthermore, the presence of hyperparameters in online models exacerbates this issue. These parameters are typically fixed and cannot be dynamically adjusted by the user in response to the evolving data distribution. This paper introduces Online Classification with Weighted Average (OLC-WA), an adaptive, hyperparameter-free online classification model equipped with an automated optimization mechanism. OLC-WA operates by blending incoming data streams with an existing base model. This blending is facilitated by an exponentially weighted moving average. Furthermore, an integrated optimization mechanism dynamically detects concept drift, quantifies its magnitude, and adjusts the model based on the observed data stream characteristics. This approach empowers the model to effectively adapt to evolving data distributions within streaming environments. Rigorous empirical evaluation across diverse benchmark datasets shows that OLC-WA achieves performance comparable to batch models in stationary environments, maintaining accuracy within 1-3%, and surpasses leading online baselines by 10-25% under drift, demonstrating its effectiveness in adapting to dynamic data streams.