# Neural Greedy Pursuit for Feature Selection

arXiv: https://arxiv.org/abs/2207.09390 · HTML: https://mlsanity.com/paper/2207.09390/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 24 references that are in the map.
- First posted on arXiv 19 Jul 2022.

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

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [Learning Important Features Through Propagating Activation Differences](https://mlsanity.com/paper/1704.02685/) (2017)
- [Deep Learning in Neural Networks: An Overview](https://mlsanity.com/paper/1404.7828/) (2014)
- [A Benchmark for Interpretability Methods in Deep Neural Networks](https://mlsanity.com/paper/1806.10758/) (2018)
- [XBART: Accelerated Bayesian Additive Regression Trees](https://mlsanity.com/paper/1810.02215/) (2018)
- [SSFN -- Self Size-estimating Feed-forward Network with Low Complexity, Limited Need for Human Intervention, and Consistent Behaviour across Trials](https://mlsanity.com/paper/1905.07111/) (2019)

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

- [Sequential Attention for Feature Selection](https://mlsanity.com/paper/2209.14881/) (2022)

## Abstract (arXiv)

We propose a greedy algorithm to select N important features among P input features for a non-linear prediction problem. The features are selected one by one sequentially, in an iterative loss minimization procedure. We use neural networks as predictors in the algorithm to compute the loss and hence, we refer to our method as neural greedy pursuit (NGP). NGP is efficient in selecting N features when N ll P, and it provides a notion of feature importance in a descending order following the sequential selection procedure. We experimentally show that NGP provides better performance than several feature selection methods such as DeepLIFT and Drop-one-out loss. In addition, we experimentally show a phase transition behavior in which perfect selection of all N features without false positives is possible when the training data size exceeds a threshold.