Using cellular automata for improving KNN based spam filtering

Abstract EN

As rapid growth over the Internet nowadays, electronic mail (e-mails) has become a popular communication tool.

However, junk mail also, known as Spam has increasingly become apart of life for users as well as internet service providers.

To address this problem, many solutions have been proposed in the last decade.

Currently, content-based anti-spam filtering methods are an important issue ; the spam filtering is considered as a special case of binary text categorization.

Many machine learning techniques have been developed and applied to classify email as spam or non-spam.

In this paper, we proposed an enhanced K-Nearest Neighbours (KNN) method called Cellular Automaton Combined with KNN (CA-KNN) for spam filtering.

In our proposed method, a cellular automaton is used to identify which instances in training set should be selected to classify a new e-mail ; CA-KNN selects the nearest neighbours not from the whole training set, but only from a reduced subset selected by a cellular automaton.