# Nonparametric IPSS: fast, flexible feature selection with false discovery control

## TL;DR

This paper introduces a new nonparametric feature selection method that controls false discoveries and outperforms existing methods in identifying relevant features in high-dimensional data.

## Contribution

The novel contribution is a nonparametric feature selection method with finite-sample false discovery control using IPSS applied to arbitrary importance scores.

## Key findings

- IPSSGB and IPSSRF accurately control the false discovery rate in nonlinear simulations with RNA sequencing data.
- The proposed methods detect more true positives than existing feature selection approaches.
- IPSSGB and IPSSRF are efficient, running in under 20 seconds for datasets with 500 samples and 5000 features.

## Abstract

Feature selection is a critical task in machine learning and statistics. However, existing feature selection methods either (i) rely on parametric methods such as linear or generalized linear models, (ii) lack theoretical false discovery control, or (iii) identify few true positives.

We introduce a general feature selection method with finite-sample false discovery control based on applying integrated path stability selection (IPSS) to arbitrary feature importance scores. The method is nonparametric whenever the importance scores are nonparametric, and it estimates q-values, which are better suited to high-dimensional data than P-values. We focus on two special cases using importance scores from gradient boosting (IPSSGB) and random forests (IPSSRF). Extensive nonlinear simulations with RNA sequencing data show that both methods accurately control the false discovery rate and detect more true positives than existing methods. Both methods are also efficient, running in under 20 s when there are 500 samples and 5000 features. We apply IPSSGB and IPSSRF to detect microRNAs and genes related to cancer, finding that they yield better predictions with fewer features than existing approaches.

All code and data used in this work are available on GitHub (https://github.com/omelikechi/ipss_bioinformatics) and permanently archived on Zenodo (https://doi.org/10.5281/zenodo.15335289). A Python package for implementing IPSS is available on GitHub (https://github.com/omelikechi/ipss) and PyPI (https://pypi.org/project/ipss/). An R implementation of IPSS is also available on GitHub (https://github.com/omelikechi/ipssR).