RSSI-based Bayesian anti-spam filtering algorithm
Chen Tie-ju · Jisuanji gongcheng yu sheji · 2015
When Bayesian algorithm is applied in spam filtering,Bernoulli model's accuracy is low and can not distinguish the importance of text features,and the multinomial model has larger computation.In addition,it is a waste of time in calculating unrelated feature elements and this model is sensitive to low frequency elements.For these shortcomings,an improved feature extraction algorithm named RSSI was proposed,which not only reduced the amount of computation,but also improved the classification performance by calculating and comparing the occurrence frequency of feature items,so that overfitting phenomenon was reduced.Experimental results show that compared with early nave Bayes algorithm and SVM algorithm,the RSSI algorithm can significantly reduce the classification time and the probability of misjudging legitimate emails.