Research on Spam Filtering Technology Based on IMI-WNB Algorithm
摘要
The application of Mutual Information(MI) and Naive Bayes(NB) algorithm to spam filtering is faced with feature redundancy and invalid independence assumption.To address the problem,this paper proposes an Improved Mutual Information-Weighted Naive Bayes(IMI-WNB) algorithm.As for the low efficiency of mutual information,an improved feature selection algorithm based on MI is proposed by introducing the word frequency factor and inter-class difference factor in order to achieve more efficient feature dimensionality reduction.To solve the problem of independence assumption of NB classification algorithm,the Improved Mutual Information(IMI) value is used for feature weighting in NB classification,which eliminates the adverse effect of part of the NB conditional independence assumption on mail classification.The experimental results show that compared with the traditional NB algorithm,the proposed algorithm improves the accuracy,recall rate and stability of spam filtering.