Feature selection with clustering probabilistic particle swarm optimization

Dealing with high-dimensional data poses a significant challenge in machine learning. To address this issue, researchers have proposed feature selection as a viable solution. Due to the intricate search space involved in feature selection, swarm intelligence algorithms have gained popularity for their exceptional search capabilities. This study introduces a method called Clustering Probabilistic Particle Swarm Optimization (CPPSO) to revolutionize the traditional particle swarm optimization approach by incorporating probabilities to represent velocity and incorporating an elitism mechanism. Furthermore, CPPSO employs a clustering strategy based on the K-means algorithm, utilizing the Hamming distance to divide the population into two sub-populations to improve the performance. To assess CPPSO's performance, a comparative analysis is conducted against seven existing algorithms using twenty diverse datasets. These datasets are all based on real-world problems. Fifteen of these are frequently used in feature selection research, while the remaining five comprise imbalanced datasets as well as multi-label datasets. The experimental results demonstrate the superiority of CPPSO across a range of evaluation criteria, surpassing the performance of the comparative algorithms on the majority of the datasets.