This Python module implements hyperparameter optimization using Particle Swarm Optimization (PSO) for various machine learning algorithms in classification task. PSO is a population-based optimization technique inspired by the social behavior of birds flocking or fish schooling.

The PSOOptimizer class provided in this module allows users to optimize hyperparameters for four different types of machine learning algorithms:

• K-Nearest Neighbors (KNN)
• Random Forest (RF)
• Decision Tree (DT)
• Support Vector Classifier (SVC)

The optimization process aims to find the best set of hyperparameters that maximize the accuracy of the respective classifier on a given dataset.

• Python 3.x
• Required Python packages: numpy, joblib, scikit-learn, tqdm

Make sure to install these dependencies using pip before using the module.

1. Install the pso-optimizer library:

pip install pso-optimizer

2. Example usage is in main.py file.

Files

• main.py: The main script to run PSO hyperparameter optimization.
• pso_optimizer.py: Contains the PSOOptimizer class for PSO optimization.
• hyperparameter_mappings.py: Contains mappings for hyperparameters used in different machine learning models.
• README.md: This file.

The implementation of PSO hyperparameter optimization is inspired by the paper "The Particle Swarm — Explosion, Stability, and Convergence in a Multidimensional Complex Space" by Clerc and Kennedy.

If you use this package in your work, please cite it using the following information: @software{pso_optimizer, author = {Mert Bayraktar}, year = {2024}, publisher = {GitHub}, journal = {GitHub repository}, howpublished = {\url{https://github.com/mBayraktar12/PSO-Hyperparameter-Selection/tree/main}}, version = {1.0.0} }