Abstract:
Spam is a key problem in electronic
communication, including large-scale email
systems. Classification of spam emails is a
significant operation in email system. The efficiency
of this process is determined by many factors such
as number of features, feature selection techniques,
representation of symbols and classifier. This paper
focuseson email classifier with Multilayer
Perceptron(MLP) approach for spam and ham
mails classification. The systemis used termfrequency
and inverse document frequency (tf-idf)
and fisher score feature selection methods at
preprocessing. These methods allow selecting
relevant features and adding benefit in terms of
improvisation in accuracy and reduced time
complexity to email classification system.
