Email Classification Using Data Reduction Method
Classifying user emails correctly from penetration of spam is an important research issue for anti-spam researchers. This paper has presented an effective and efficient email classification technique based on data filtering method. In their testing they have introduced an innovative filtering technique using Instance Selection Method (ISM) to reduce the pointless data instances from training model and then classify the test data. The objective of ISM is to identify which instances (examples, patterns) in email corpora should be selected as representatives of the entire dataset, without significant loss of information.