OPTIMIZED SPAM CLASSIFICATION APPROACH WITH NEGATIVE SELECTION ALGORITHM
Ismaila Idris, Ali Selamat · Journal of Theoretical and Applied Information Technology · 2012
This paper initializes a two element concentration vector as a feature vector for classification and spam detection. Negative selection algorithm proposed by the immune system in solving problems in spam detection is used to distinguish spam from non-spam (self from non-self). Self concentration and non-s elf concentration are generated to form two element concentration vectors. In this approach to e-mail classification, the e-mail are considered as an opt imization problem using genetic algorithm to minimize the cost function that was generated and then classific ation of these cost function shall aid in creating a classifier. This classifier will aid in the new for mation of algorithm that comprises of both greater efficiency detector rate and also speedy detection of spam e-m ail. The algorithm implementation of the research w ork shall come in stages were spam and non-spam are detected in all phases for an efficient classifier.