Abstract:
Spam is one of the main problems in e-mails
communication. Naïve Bayesian (NB) learning is
useful algorithm for constructing Myanmar
spam corpus (SCorpus) from pre-defined spam
and ham e-mails. SCorpus is built on the
assumption that the characteristics of e-mails in
the training dataset. Content-based analysis is
particularly effective in filtering spam. NB plays
a critical role in probabilistic learning and
calculates the probability of an e-mail being
spam based on its contents. The motivation for
this paper is to find a solution for the Internet
users in Myanmar e-mails received every day in
their mailboxes. There is no standard
implementation for treatment of Myanmar emails.
So, a classification filter for the e-mails
should be proposed with SCorpus. NB approach
is being popular for learning corpus. To filter the
spam e-mail, paper MSFNBLA is applied for
classifying the incoming e-mail is spam or ham.
