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

翻译：本研究提出了一种新颖方法MBO-NB，该方法利用迁徙鸟类优化（MBO）耦合朴素贝叶斯作为内部分类器，以解决具有大量特征的文本分类中的特征选择挑战。聚焦于计算效率，我们使用信息增益算法预处理原始数据，将特征数量从平均62221个战略性地减少至2089个。实验表明，与其他现有技术相比，MBO-NB在特征缩减方面具有卓越有效性，并显著提高了分类准确性。朴素贝叶斯在MBO中的成功集成提供了一种全面解决方案。在与粒子群优化（PSO）的单独比较中，MBO-NB在四种设置下平均性能提升6.9%。本研究为改进特征选择方法提供了宝贵见解，为文本分类提供了一种可扩展且有效的解决方案。