# Faster feature selection with a Dropping Forward-Backward algorithm

arXiv: https://arxiv.org/abs/1910.08007 · HTML: https://mlsanity.com/paper/1910.08007/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Oct 2019.

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

- [Combining datasets to increase the number of samples and improve model fitting](https://mlsanity.com/paper/2210.05165/) (2022)
- [Parallel feature selection based on the trace ratio criterion](https://mlsanity.com/paper/2203.01635/) (2022)

## Abstract (arXiv)

In this era of big data, feature selection techniques, which have long been proven to simplify the model, makes the model more comprehensible, speed up the process of learning, have become more and more important. Among many developed methods, forward and stepwise feature selection regression remained widely used due to their simplicity and efficiency. However, they all involving rescanning all the un-selected features again and again. Moreover, many times, the backward steps in stepwise deem unnecessary, as we will illustrate in our example. These remarks motivate us to introduce a novel algorithm that may boost the speed up to 65.77% compared to the stepwise procedure while maintaining good performance in terms of the number of selected features and error rates. Also, our experiments illustrate that feature selection procedures may be a better choice for high-dimensional problems where the number of features highly exceeds the number of samples.