# A Contrast Based Feature Selection Algorithm for High-dimensional Data set in Machine Learning

arXiv: https://arxiv.org/abs/2401.07482 · HTML: https://mlsanity.com/paper/2401.07482/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 41 references that are in the map.
- First posted on arXiv 15 Jan 2024.

## Builds on (most cited first, 5 of 5)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Concrete Autoencoders for Differentiable Feature Selection and Reconstruction](https://mlsanity.com/paper/1901.09346/) (2019)
- [LassoNet: A Neural Network with Feature Sparsity](https://mlsanity.com/paper/1907.12207/) (2019)
- [Challenges of Feature Selection for Big Data Analytics](https://mlsanity.com/paper/1611.01875/) (2016)
- [Variable selection with false discovery rate control in deep neural networks](https://mlsanity.com/paper/1909.07561/) (2019)

## Abstract (arXiv)

Feature selection is an important process in machine learning and knowledge discovery. By selecting the most informative features and eliminating irrelevant ones, the performance of learning algorithms can be improved and the extraction of meaningful patterns and insights from data can be facilitated. However, most existing feature selection methods, when applied to large datasets, encountered the bottleneck of high computation costs. To address this problem, we propose a novel filter feature selection method, ContrastFS, which selects discriminative features based on the discrepancies features shown between different classes. We introduce a dimensionless quantity as a surrogate representation to summarize the distributional individuality of certain classes, based on this quantity we evaluate features and study the correlation among them. We validate effectiveness and efficiency of our approach on several widely studied benchmark datasets, results show that the new method performs favorably with negligible computation in comparison with other state-of-the-art feature selection methods.