IBNE_ScalableFS

This repository contains code for a feature selection pipeline designed for machine learning tasks. The main entry point is pipeline.py, which leverages modular functions provided in the feature_selection package. Pipeline parameters are configured in opt.py and are passed as command-line arguments when executing the pipeline.

The work has been published: check it out https://doi.org/10.1016/j.compbiomed.2025.110812.

Table of Contents

1. Overview
2. Directory Structure
3. Installation
4. Usage
5. Examples
6. Citation
7. Contributing
8. License

Overview

The feature selection pipeline in this repository is designed to improve the efficiency and accuracy of machine learning in clinical environment models by identifying and selecting the most relevant features.

All feature selection logic is encapsulated in the feature_selection package, enabling users to modify and extend functionalities easily.

Directory Structure

├── pipeline.py # Main script to run the feature selection pipeline 
├── feature_selection/ # Package containing feature selection methods 
│ ├── init.py # Initializes the package
| ├── featureSelection.py # Contains the feature selection functions 
│ ├── general.py # Importing function and basic 
│ ├── models.py # training and testing
│ ├── plots.py # Plot wrapper
│ └── statistics.py # Wrapper for statistic functions
├── opt.py # Pipeline configuration parameters 
├── SINPAIN # directory containing results from SINPAIN dataset
├── SINPAIN # directory containing results from SINPAIN dataset
└── README.md # Project documentation

Installation

To install dependencies, clone the repository and run:

bash git clone https://github.com/maruotto/IBNE_ScalableFS.git cd IBNE_ScalableFS pip install -r requirements.txt

Usage

To run the feature selection pipeline, use pipeline.py with parameters specified in cli, they are coded in opt.py. The only parameter that can't be controlled through the cli are the seeds in input to the algorithm. They are contained in models.py

Citation

If you use this codebase in your research, please consider citing us. For further information, please write to ida23@ru.is

@article{MARUOTTO2025110812, title = {Feature Selection in Healthcare Datasets: Towards a Generalizable Solution}, journal = {Computers in Biology and Medicine}, volume = {196}, pages = {110812}, year = {2025}, issn = {0010-4825}, doi = {https://doi.org/10.1016/j.compbiomed.2025.110812}, url = {https://www.sciencedirect.com/science/article/pii/S0010482525011631}, author = {Ida Maruotto and Federica Kiyomi Ciliberti and Paolo Gargiulo and Marco Recenti}, keywords = {Feature selection, Artificial intelligence, Machine learning, Dimensionality reduction, Healthcare dataset, Scalability, Biomedical signals}, }

By citing our work, you help support the open-source and academic community, making it easier for others to find and build on this project.

Contributing

Contributions are welcome! Feel free to open an issue or submit a pull request for any improvements or bug fixes.

Licence

This project is licensed under the MIT License. See the LICENSE file for details.