# Synthetic Non-stationary Data Streams for Recognition of the Unknown

arXiv: https://arxiv.org/abs/2505.13745 · HTML: https://mlsanity.com/paper/2505.13745/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 38 references that are in the map.
- First posted on arXiv 19 May 2025.

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

- [Recent Advances in Open Set Recognition: A Survey](https://mlsanity.com/paper/1811.08581/) (2018)
- [Concept Drift Detection from Multi-Class Imbalanced Data Streams](https://mlsanity.com/paper/2104.10228/) (2021)
- [Incremental Learning from Low-labelled Stream Data in Open-Set Video Face Recognition](https://mlsanity.com/paper/2012.09571/) (2020)
- [Event Detection from Social Media Stream: Methods, Datasets and Opportunities](https://mlsanity.com/paper/2306.16495/) (2023)
- [OEBench: Investigating Open Environment Challenges in Real-World Relational Data Streams](https://mlsanity.com/paper/2308.15059/) (2023)
- [Unsupervised Concept Drift Detection based on Parallel Activations of Neural Network](https://mlsanity.com/paper/2404.07776/) (2024)
- [Taking Class Imbalance Into Account in Open Set Recognition Evaluation](https://mlsanity.com/paper/2402.06331/) (2024)
- [Resilience to the Flowing Unknown: an Open Set Recognition Framework for Data Streams](https://mlsanity.com/paper/2411.00876/) (2024)

## Built on by (most cited first, 4 of 4)

- [CaDrift: A Time-dependent Causal Generator of Drifting Data Streams](https://mlsanity.com/paper/2602.20329/) (2026)
- [Open World Autoencoding Drift Detection with Novel Class Recognition in Tabular Non-stationary Data Streams](https://mlsanity.com/paper/2605.29834/) (2026)
- [How well does Classification Accuracy capture Concept Drift Detection Quality? An overview of Concept Drift Detection evaluation](https://mlsanity.com/paper/2605.31186/) (2026)
- [Concept Drift from a Causal Perspective](https://mlsanity.com/paper/2609.25340/) (2026)

## Abstract (arXiv)

The problem of data non-stationarity is commonly addressed in data stream processing. In a dynamic environment, methods should continuously be ready to analyze time-varying data -- hence, they should enable incremental training and respond to concept drifts. An equally important variability typical for non-stationary data stream environments is the emergence of new, previously unknown classes. Often, methods focus on one of these two phenomena -- detection of concept drifts or detection of novel classes -- while both difficulties can be observed in data streams. Additionally, concerning previously unknown observations, the topic of open set of classes has become particularly important in recent years, where the goal of methods is to efficiently classify within known classes and recognize objects outside the model competence. This article presents a strategy for synthetic data stream generation in which both concept drifts and the emergence of new classes representing unknown objects occur. The presented research shows how unsupervised drift detectors address the task of detecting novelty and concept drifts and demonstrates how the generated data streams can be utilized in the open set recognition task.