# Learning with Feature Evolvable Streams

arXiv: https://arxiv.org/abs/1706.05259 · HTML: https://mlsanity.com/paper/1706.05259/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jun 2017.

## Built on by (most cited first, 15 of 15)

- [Open-environment Machine Learning](https://mlsanity.com/paper/2206.00423/) (2022)
- [Learning with Recoverable Forgetting](https://mlsanity.com/paper/2207.08224/) (2022)
- [Fine-Tuning Attention Modules Only: Enhancing Weight Disentanglement in Task Arithmetic](https://mlsanity.com/paper/2407.07089/) (2024)
- [Adversarial Robustness of Streaming Algorithms through Importance Sampling](https://mlsanity.com/paper/2106.14952/) (2021)
- [Aux-Drop: Handling Haphazard Inputs in Online Learning Using Auxiliary Dropouts](https://mlsanity.com/paper/2303.05155/) (2023)
- [Seeing Differently, Acting Similarly: Heterogeneously Observable Imitation Learning](https://mlsanity.com/paper/2106.09256/) (2021)
- [SEFD: Semantic-Enhanced Framework for Detecting LLM-Generated Text](https://mlsanity.com/paper/2411.12764/) (2024)
- [Realistic Evaluation of TabPFN v2 in Open Environments](https://mlsanity.com/paper/2505.16226/) (2025)
- [Online Deep Learning from Doubly-Streaming Data](https://mlsanity.com/paper/2204.11793/) (2022)
- [Online Learning under Haphazard Input Conditions: A Comprehensive Review and Analysis](https://mlsanity.com/paper/2404.04903/) (2024)
- [Transfer-Learning Across Datasets with Different Input Dimensions: An Algorithm and Analysis for the Linear Regression Case](https://mlsanity.com/paper/2202.05069/) (2022)
- [Theoretically Guaranteed Distribution Adaptable Learning](https://mlsanity.com/paper/2411.02921/) (2024)
- [Haphazard Inputs as Images in Online Learning](https://mlsanity.com/paper/2504.02912/) (2025)
- [ICAFS: Inter-Client-Aware Feature Selection for Vertical Federated Learning](https://mlsanity.com/paper/2504.10851/) (2025)
- [Feature Incremental Clustering with Generalization Bounds](https://mlsanity.com/paper/2603.21590/) (2026)

## Abstract (arXiv)

Learning with streaming data has attracted much attention during the past few years. Though most studies consider data stream with fixed features, in real practice the features may be evolvable. For example, features of data gathered by limited-lifespan sensors will change when these sensors are substituted by new ones. In this paper, we propose a novel learning paradigm: Feature Evolvable Streaming Learning where old features would vanish and new features would occur. Rather than relying on only the current features, we attempt to recover the vanished features and exploit it to improve performance. Specifically, we learn two models from the recovered features and the current features, respectively. To benefit from the recovered features, we develop two ensemble methods. In the first method, we combine the predictions from two models and theoretically show that with the assistance of old features, the performance on new features can be improved. In the second approach, we dynamically select the best single prediction and establish a better performance guarantee when the best model switches. Experiments on both synthetic and real data validate the effectiveness of our proposal.