# Are screening methods useful in feature selection? An empirical study

arXiv: https://arxiv.org/abs/1809.05465 · HTML: https://mlsanity.com/paper/1809.05465/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Sep 2018.

## Built on by (most cited first, 2 of 2)

- [Online Feature Screening for Data Streams with Concept Drift](https://mlsanity.com/paper/2104.02883/) (2021)
- ["FRAME: Forward Recursive Adaptive Model Extraction-A Technique for Advance Feature Selection"](https://mlsanity.com/paper/2501.11972/) (2025)

## Abstract (arXiv)

Filter or screening methods are often used as a preprocessing step for reducing the number of variables used by a learning algorithm in obtaining a classification or regression model. While there are many such filter methods, there is a need for an objective evaluation of these methods. Such an evaluation is needed to compare them with each other and also to answer whether they are at all useful, or a learning algorithm could do a better job without them. For this purpose, many popular screening methods are partnered in this paper with three regression learners and five classification learners and evaluated on ten real datasets to obtain accuracy criteria such as R-square and area under the ROC curve (AUC). The obtained results are compared through curve plots and comparison tables in order to find out whether screening methods help improve the performance of learning algorithms and how they fare with each other. Our findings revealed that the screening methods were useful in improving the prediction of the best learner on two regression and two classification datasets out of the ten datasets evaluated.