Chinese Spam Message Filtering Based on Text Weighted KNN Algorithm
摘要
In view of the drawback that the decision rules of classification regard for K Nearest Neighbor(KNN),the importance of every sample as the same,the classification results are not good.This paper proposes a method based on the text weighted KNN text classification algorithm and applies it to the classification of spam messages.After feature selection,considering the influence of frequency of feature words appearing in the text on text importance,the paper puts forward the first weighting formula.It uses association rule algorithm to mine frequent term sets from the spam message text and puts forward the second formula.Finally,it uses the two weighting formulas for the composite weighting on every message text so as to distinguish the influence of every training sample on category determination,thus improving on the classification decision rules.Experimental results show that the method has a promotion in accuracy,recall rate and F1 value which are important indexes compared with the un-improved KNN classification of spam filtering.