Guided Particle Swarm Optimization for Feature Selection: Application to Cancer Genome Data

Feature selection is a crucial step in the data preprocessing stage of machine learning. It involves selecting a subset of relevant features for use in model construction. Feature selection helps in improving model performance by reducing overfitting, enhancing generalization, and decreasing computational cost. Techniques for feature selection can be broadly classified into filter methods, wrapper methods, and embedded methods. This paper presents a feature selection method based on Particle Swarm Optimization (PSO). The proposed algorithm makes use of a guided particle scheme whereby three filter-based methods are incorporated. The proposed algorithm addresses the issue of premature convergence to global optima compared to other PSO feature-based methods. In addition, the algorithm is tested on very-high-dimensional genome data that include up to 44,909 features. Results of an experimental comparison with other state-of-the-art feature selection algorithms show that the proposed algorithm produces overall better results.