Abstract：The spread of spam brings a great deal of disturbance to e-mail
users. In order to improve the efficiency of spam filtering and
solve the problem that some attributes extracted from the e-mail
header are null, we first applied the knowledge reduction algorithm
and the sample recognition algorithm based on incomplete information
system of Rough Set theory to spam filtering on e-mail header. In
this paper, we proposed an improved non-symmetric similarity
relation and defined the attribute complete importance for attribute
reduction. Experimental results showed that the algorithm got higher
recall and precision, lower fake recognition than other algorithms.
Yuanning Liu;Ye Han;Xiaodong Zhu;Fei He. The Application of Incomplete Information System of Rough Set Theory in Spam Filtering[J]. , 2014, 11(11): 3847-3856.
Yuanning Liu;Ye Han;Xiaodong Zhu;Fei He. The Application of Incomplete Information System of Rough Set Theory in Spam Filtering. , 2014, 11(11): 3847-3856.
2014, Vol. 11 
