  This research introduces a novel approach, MBO-NB, that leverages Migrating
Birds Optimization (MBO) coupled with Naive Bayes as an internal classifier to
address feature selection challenges in text classification having large number
of features. Focusing on computational efficiency, we preprocess raw data using
the Information Gain algorithm, strategically reducing the feature count from
an average of 62221 to 2089. Our experiments demonstrate MBO-NB's superior
effectiveness in feature reduction compared to other existing techniques,
emphasizing an increased classification accuracy. The successful integration of
Naive Bayes within MBO presents a well-rounded solution. In individual
comparisons with Particle Swarm Optimization (PSO), MBO-NB consistently
outperforms by an average of 6.9% across four setups. This research offers
valuable insights into enhancing feature selection methods, providing a
scalable and effective solution for text classification
