Multiple classifications for detecting Spam email by novel consultation algorithm

Much work and many transactions these days are done via email. Email is a powerful tool for communication that saves both time and cost. However, due to the growth of social networks and advertisers, the number of unwanted emails sent to a cumulative mass of users continues to grow. Junk email that is sent in a bulk fashion is called UBE or Spam email, for short. To date many algorithms have been devised to flag junk or Spam email from legitimate or Ham email. However, none of these algorithms has been 100% accurate. Recent studies of clustering have pointed to hybrid methods that are powerful, stable, accurate, and more common than previous ones. Inspired by the processes of the Public Consultation and Voting System, this paper will present a novel algorithm to accurately flag junk email and to separate Spam from Ham email. The error rate of a single optimization algorithm will improve by 39% using of our consultation and voting (CAV) algorithm.