Spam filtering method based on improved KNN
Jingdong Xu · Computer Engineering and Applications Journal · 2007
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.