Improvement of Information Gain in Spam Filtering
Zhai Jun-chan · 2014
The paper put forward a kind of improved information gain for the feature words selection in spam filtering. Firstly,defined gain ratio according to the probability of feature words,and then amplifed or weakened the amount of information of the feature words for classification,thereby improving the calculation method of category conditional entropy.Finally,combining with the naive Bayes decision method of maximum a posteriori hypothesis,carried out an experiment on the English Corpus to analyze the algorithm through recall,correct,accuracy and error.The experimental results show that the improved algorithm can enhance classification precision and reduce user loss.