# OPTWIN: Drift identification with optimal sub-windows

arXiv: https://arxiv.org/abs/2305.11942 · HTML: https://mlsanity.com/paper/2305.11942/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 35 references that are in the map.
- First posted on arXiv 19 May 2023.

## Builds on (most cited first, 8 of 8)

- [Learning under Concept Drift: A Review](https://mlsanity.com/paper/2004.05785/) (2020)
- [Online Learning: A Comprehensive Survey](https://mlsanity.com/paper/1802.02871/) (2018)
- [River: machine learning for streaming data in Python](https://mlsanity.com/paper/2012.04740/) (2020)
- [From Concept Drift to Model Degradation: An Overview on Performance-Aware Drift Detectors](https://mlsanity.com/paper/2203.11070/) (2022)
- [Optimistic Whittle Index Policy: Online Learning for Restless Bandits](https://mlsanity.com/paper/2205.15372/) (2022)
- [Reward Design For An Online Reinforcement Learning Algorithm Supporting Oral Self-Care](https://mlsanity.com/paper/2208.07406/) (2022)
- [InstanceFormer: An Online Video Instance Segmentation Framework](https://mlsanity.com/paper/2208.10547/) (2022)
- [Online Reinforcement Learning with Uncertain Episode Lengths](https://mlsanity.com/paper/2302.03608/) (2023)

## Built on by (most cited first, 1 of 1)

- [Convergence Analysis of Decentralized ASGD](https://mlsanity.com/paper/2309.03754/) (2023)

## Abstract (arXiv)

Online Learning (OL) is a field of research that is increasingly gaining attention both in academia and industry. One of the main challenges of OL is the inherent presence of concept drifts, which are commonly defined as unforeseeable changes in the statistical properties of an incoming data stream over time. The detection of concept drifts typically involves analyzing the error rates produced by an underlying OL algorithm in order to identify if a concept drift occurred or not, such that the OL algorithm can adapt accordingly. Current concept-drift detectors perform very well, i.e., with low false negative rates, but they still tend to exhibit high false positive rates in the concept-drift detection. This may impact the performance of the learner and result in an undue amount of computational resources spent on retraining a model that actually still performs within its expected range. In this paper, we propose OPTWIN, our "OPTimal WINdow" concept drift detector. OPTWIN uses a sliding window of events over an incoming data stream to track the errors of an OL algorithm. The novelty of OPTWIN is to consider both the means and the variances of the error rates produced by a learner in order to split the sliding window into two provably optimal sub-windows, such that the split occurs at the earliest event at which a statistically significant difference according to either the t- or the f-tests occurred. We assessed OPTWIN over the MOA framework, using ADWIN, DDM, EDDM, STEPD and ECDD as baselines over 7 synthetic and real-world datasets, and in the presence of both sudden and gradual concept drifts. In our experiments, we show that OPTWIN surpasses the F1-score of the baselines in a statistically significant manner while maintaining a lower detection delay and saving up to 21% of time spent on retraining the models.