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《Computer Engineering and Applications》 2007-25
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Spam filtering method based on improved KNN

TIAN Ze,YAN Song-yuan,XU Jing-dong School of Information Science and Technology,Nankai University,Tianjin 300071,China  
This paper presents a fast text classification algorithm based on KNN(K Nearest Neighbor).It increases the classification efficiency by compressing training samples and eliminating comparisons between similarities,while maintaining high classification performance of the original KNN algorithm.The experiment shows that in E-mail filter system,the new algorithm has a better classification performance than Binary Bernoulli Model or Multinomial Model,both of which are based on Naive Bayes classifier.And its computational complexity of classification is equal to these two algorithms,so it can be applied to real-time E-mail filtering.
【Fund】: 天津市信息化项目(No.042023012)。
【CateGory Index】: TP393.098
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【Citations】
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【Co-citations】
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