The Evaluation of Ordered Features for SMS Spam Filtering

In this work we propose a method to capture the writing style of spams and non-spam messages by preserving the sequentiality of the text in the feature space. To be more specific, we propose to build the feature vector considering the features apparition order in the text. We extract features from messages by applying three techniques: Extrinsic Information, Sequential Labeling Extraction and Term Clustering. In doing so, the method presents low dimensional feature space that shows competitive classification accuracy for the tested classifiers.