Enhancing kNN classification with crow search optimization for dynamic text-based data categorization

An integration of the k-nearest neighbors (kNN) algorithm with crow search optimization (CSO) to tackle the challenges of text-based data classification was proposed. The kNN algorithm, is combined with the CSO's robust optimization capabilities to dynamically select the optimal 'k' value and feature set, enhancing the adaptability and accuracy of text classification. The evolving nature of text data, particularly in high-volume academic and business contexts, demands efficient and adaptive classification methods to handle varying data distributions and feature relevance. The kNN-CSO method addresses these requirements by leveraging CSO to fine-tune kNN parameters, ensuring high-performance metrics across different datasets. Initial results demonstrate the method's efficacy, particularly in handling ambiguities and optimizing classification under varying conditions. The results demonstrate the efficacy of this method, yielding an accuracy of up to 96%, precision up to 95%, and an F1-score reaching 95%, significantly enhancing the model's adaptability to new or evolving data. This approach not only improves classification accuracy but also enhances the model's ability to adapt to new or evolving data, providing a significant advancement in automated document processing and categorization.