Research on Optimization of Naive Bayes Algorithm for Spam Filtering

With the rapid development of the Internet, email as an important communication tool faces increasingly severe spam problems. This paper proposes a series of optimization and improvement solutions targeting several key issues in the application of traditional Naive Bayes algorithm for spam filtering. The research first systematically analyzes the theoretical foundation of Naive Bayes algorithm in text classification and its applicability in spam identification, thoroughly investigating main factors affecting algorithm performance such as data imbalance, feature selection, and high-dimensional sparsity. Building on this foundation, the paper presents comprehensive improvement strategies from four dimensions: data preprocessing, feature selection, model structure, and parameter optimization, including innovative solutions like improved information gain feature selection methods, dynamic weight adjustment mechanisms, and semi-Naive Bayes models. Through comparative experimental validation, the optimized algorithm achieves significant performance enhancement in classification, effectively reducing misjudgment rates while improving identification capability for new types of spam. This research not only provides more effective technical solutions for spam filtering but also offers valuable references for extending Naive Bayes algorithm to other text classification tasks.