Optimizing Feature Selection in Spam Email Detection using Co-Evolutionary Algorithms

This research project implements a novel co-evolutionary feature selection method for the classification of spam emails. Through the utilization of co-evolutionary algorithms, the study analyzes and compares the outcomes with other established feature selection methods in machine learning. The research demonstrates how bio-inspired algorithms can effectively identify the most relevant features for text classification tasks, leading to improved accuracy and reduced computational complexity. The project includes comprehensive experimental analysis and visualization of results.