# Online Feature Screening for Data Streams with Concept Drift

arXiv: https://arxiv.org/abs/2104.02883 · HTML: https://mlsanity.com/paper/2104.02883/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 63 references that are in the map.
- First posted on arXiv 7 Apr 2021.

## Builds on (most cited first, 6 of 6)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Decision Forests, Convolutional Networks and the Models in-Between](https://mlsanity.com/paper/1603.01250/) (2016)
- [Leveraging Model Inherent Variable Importance for Stable Online Feature Selection](https://mlsanity.com/paper/2006.10398/) (2020)
- [Neural Rule Ensembles: Encoding Sparse Feature Interactions into Neural Networks](https://mlsanity.com/paper/2002.04319/) (2020)
- [Are screening methods useful in feature selection? An empirical study](https://mlsanity.com/paper/1809.05465/) (2018)
- [Updating Formulas and Algorithms for Computing Entropy and Gini Index from Time-Changing Data Streams](https://mlsanity.com/paper/1403.6348/) (2014)

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

- [Exploring Vision Neural Network Pruning via Screening Methodology](https://mlsanity.com/paper/2502.07189/) (2025)

## Abstract (arXiv)

Screening feature selection methods are often used as a preprocessing step for reducing the number of variables before training step. Traditional screening methods only focus on dealing with complete high dimensional datasets. Modern datasets not only have higher dimension and larger sample size, but also have properties such as streaming input, sparsity and concept drift. Therefore a considerable number of online feature selection methods were introduced to handle these kind of problems in recent years. Online screening methods are one of the categories of online feature selection methods. The methods that we proposed in this research are capable of handling all three situations mentioned above. Our research study focuses on classification datasets. Our experiments show proposed methods can generate the same feature importance as their offline version with faster speed and less storage consumption. Furthermore, the results show that online screening methods with integrated model adaptation have a higher true feature detection rate than without model adaptation on data streams with the concept drift property. Among the two large real datasets that potentially have the concept drift property, online screening methods with model adaptation show advantages in either saving computing time and space, reducing model complexity, or improving prediction accuracy.