# Deep Feature Selection Using a Novel Complementary Feature Mask

arXiv: https://arxiv.org/abs/2209.12282 · HTML: https://mlsanity.com/paper/2209.12282/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 40 references that are in the map.
- First posted on arXiv 25 Sep 2022.

## Builds on (most cited first, 13 of 13)

- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms](https://mlsanity.com/paper/1708.07747/) (2017)
- [Concrete Autoencoders for Differentiable Feature Selection and Reconstruction](https://mlsanity.com/paper/1901.09346/) (2019)
- [DeepPINK: reproducible feature selection in deep neural networks](https://mlsanity.com/paper/1809.01185/) (2018)
- [AFS: An Attention-based mechanism for Supervised Feature Selection](https://mlsanity.com/paper/1902.11074/) (2019)
- [Deep Feature Selection using a Teacher-Student Network](https://mlsanity.com/paper/1903.07045/) (2019)
- [Feature Selection Using Batch-Wise Attenuation and Feature Mask Normalization](https://mlsanity.com/paper/2010.13631/) (2020)
- [Fractal Autoencoders for Feature Selection](https://mlsanity.com/paper/2010.09430/) (2020)
- [Deep One-Class Classification Using Intra-Class Splitting](https://mlsanity.com/paper/1902.01194/) (2019)
- [Differentiable Feature Selection, a Reparameterization Approach](https://mlsanity.com/paper/2107.10030/) (2021)
- [Binary Stochastic Filtering: feature selection and beyond](https://mlsanity.com/paper/2007.03920/) (2020)
- [A Deep-Learning-Aided Pipeline for Efficient Post-Silicon Tuning](https://mlsanity.com/paper/2207.00336/) (2022)
- [Conditional Variable Selection for Intelligent Test](https://mlsanity.com/paper/2207.00335/) (2022)

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

- [Learning to Embed Time Series Patches Independently](https://mlsanity.com/paper/2312.16427/) (2023)

## Abstract (arXiv)

Feature selection has drawn much attention over the last decades in machine learning because it can reduce data dimensionality while maintaining the original physical meaning of features, which enables better interpretability than feature extraction. However, most existing feature selection approaches, especially deep-learning-based, often focus on the features with great importance scores only but neglect those with less importance scores during training as well as the order of important candidate features. This can be risky since some important and relevant features might be unfortunately ignored during training, leading to suboptimal solutions or misleading selections. In our work, we deal with feature selection by exploiting the features with less importance scores and propose a feature selection framework based on a novel complementary feature mask. Our method is generic and can be easily integrated into existing deep-learning-based feature selection approaches to improve their performance as well. Experiments have been conducted on benchmarking datasets and shown that the proposed method can select more representative and informative features than the state of the art.