This project applies Particle Swarm Optimization (PSO) and Dispersive Flies Optimization (DFO) techniques for feature selection. The goal is to identify the optimal subset of features from a large dataset to improve classification accuracy in machine learning tasks.
- Implemented PSO and DFO algorithms to select relevant features.
- Reduced dimensionality of datasets while enhancing model performance.
- Evaluated selected features on classification tasks to demonstrate accuracy improvements.
- Python
- NumPy, Pandas
- Scikit-learn (for classification models)
- Custom implementations of PSO and DFO
- The Dispersive Flies Optimisation (DFO) Algorithm - https://github.com/mohmaj/DFO
- Al-Rifaie, M. M. (2014). Dispersive flies optimisation. In 2014 Federated Conference on Computer Science and Information Systems (pp. 529-538). IEEE - https://www.researchgate.net/publication/267514160_Dispersive_Flies_Optimisation