The Impact of the Mode of Data Representation for the Result Quality of the Detection and Filtering of Spam
Reda Mohamed Hamou, Abdelmalek Amine · International Journal of Information Retrieval Research · 2013
Spam is now seized of the Internet in phenomenal proportions since it high represents a percentage of total emails exchanged on the Internet. In the fight against spam, the authors are interested in this article to develop a hybrid algorithm based primarily on the probabilistic model in this case Naïve Bayes for weighting the terms of the matrix term -category and second place used an algorithm of unsupervised learning (K-means) to filter two classes namely spam and ham. To determine the sensitive parameters that improve the classifications the authors are interested in studying the content of the messages by using a representation of messages by the n-gram words and characters independent of languages (because a message may be received in any language) to later decide what representation opt to get a good classification. The authors have chosen several metrics evaluation to validate their results.