Name
Abstract | ||
|---|---|---|
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 a part 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. |
Year | Venue | Keywords |
|---|---|---|
2014 | INTERNATIONAL ARAB JOURNAL OF INFORMATION TECHNOLOGY | Spam e-mail filtering, machine learning, KNN, cellular automata, instance selection |
DocType | Volume | Issue |
Journal | 11 | 4 |
ISSN | Citations | PageRank |
1683-3198 | 1 | 0.37 |
References | Authors | |
15 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fatiha Barigou | 1 | 14 | 6.76 |
| Bouziane Beldjilali | 2 | 65 | 9.29 |
| Baghdad Atmani | 3 | 70 | 18.72 |