Cultural algorithm based principal component analysis (CA-PCA) approach for handling high dimensional data

The exponential growth of high-dimensional data in various domains, such as healthcare, finance, and image processing, presents significant challenges for efficient analysis and predictive modeling. Dimensionality reduction is a key technique to address these challenges, mitigating the curse of dimensionality while preserving the most relevant information. This paper proposes an optimization-based dimensionality reduction approach that integrates principal component analysis (PCA) with cultural algorithm (CA) optimization to enhance the handling of high-dimensional datasets. PCA is employed to transform the data by extracting principal components that capture the maximum variance. However, the selection of an optimal subset of components remains crucial for maintaining model accuracy and computational efficiency. To this end, the cultural algorithm is leveraged to optimize the selection of the most informative principal components by mimicking the evolutionary process of knowledge acquisition in a cultural framework. The proposed approach is validated through experiments on various high-dimensional datasets, demonstrating its superiority in reducing data dimensionality while maintaining high classification accuracy and reducing computational costs. The results highlight the effectiveness of combining PCA with cultural algorithm optimization for dimensionality reduction, paving the way for its application in large-scale real-world problems.