Clustering-based Co-evolutionary Crow Search Algorithm for Feature Selection in High-dimensional Data

The present data collection techniques can generate thousands or even more features in a dataset. However, an excessive number of redundant features can negatively impact the learning speed and classification performance of machine learning models. Selecting important features from high-dimensional data is a challenge. To address this challenge, We propose a cooperative evolutionary algorithm based on feature clustering to partition the feature subspace for feature selection. Firstly, a clustering method based on feature similarity is employed to partition the subspaces at a lower computational cost. Then, an initialization strategy based on feature-label correlation is proposed to accelerate the convergence of the population. Finally, to reduce the dimensionality of the feature subset while ensuring the classification efficiency of the algorithm, a mutation operator based on the optimal individual is introduced to obtain higher quality solutions. The algorithm is applied to 14 classic datasets and compared with 7 advanced algorithms. Experimental results demonstrate the algorithm's ability to obtain good feature subsets.