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

arXiv: https://arxiv.org/abs/2512.12785 · HTML: https://mlsanity.com/paper/2512.12785/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 47 references that are in the map.
- First posted on arXiv 14 Dec 2025.

## Builds on (most cited first, 11 of 11)

- [Optimization Methods for Large-Scale Machine Learning](https://mlsanity.com/paper/1606.04838/) (2016)
- [Online Learning: A Comprehensive Survey](https://mlsanity.com/paper/1802.02871/) (2018)
- [Characterizing Concept Drift](https://mlsanity.com/paper/1511.03816/) (2015)
- [Online Deep Learning: Learning Deep Neural Networks on the Fly](https://mlsanity.com/paper/1711.03705/) (2017)
- [Online Continual Learning under Extreme Memory Constraints](https://mlsanity.com/paper/2008.01510/) (2020)
- [Reactive Soft Prototype Computing for Concept Drift Streams](https://mlsanity.com/paper/2007.05432/) (2020)
- [Logistic Regression: Tight Bounds for Stochastic and Online Optimization](https://mlsanity.com/paper/1405.3843/) (2014)
- [Online Boosting Adaptive Learning under Concept Drift for Multistream Classification](https://mlsanity.com/paper/2312.10841/) (2023)
- [Online AutoML: An adaptive AutoML framework for online learning](https://mlsanity.com/paper/2201.09750/) (2022)
- [Finite sample guarantees for quantile estimation: An application to detector threshold tuning](https://mlsanity.com/paper/2105.12239/) (2021)
- [Bilevel Online Deep Learning in Non-stationary Environment](https://mlsanity.com/paper/2201.10236/) (2022)

## Abstract (arXiv)

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.