Text classification is a supervised learning technique that uses labeled training data to derive a classification system (classifier) and then automatically classifies unlabelled text data using the derived classifier. In this paper, we investigate Naïve Bayesian method (NB) and Support Vector Machine algorithm (SVM) on different Arabic data sets. The bases of our comparison are the most popula...

Although there are a lot of researches about e-mail spam filters, only a few focus on the issue for SMS (Short Message Service) system, especially in Chinese. In this paper, we proposed a two-layer filter model based on Naïve Bayes classifier utilizing both some traditional filter rules and content filter technical. The experimental results illustrate that the two-layer filter model can enhance...

This paper proposes an unsupervised training approach to resolving overlapping ambiguities in Chinese word segmentation. We present an ensemble of adapted Naïve Bayesian classifiers that can be trained using an unlabelled Chinese text corpus. These classifiers differ in that they use context words within windows of different sizes as features. The performance of our approach is evaluated on a m...

Text classification is a supervised technique that uses labelled training data to learn the classification system and then automatically classifies the remaining text using the learned system. This paper investigates Naïve Bayesian algorithm based on Chi Square features selection method. The base of our comparisons are macro F1, macro recall and macro precision evaluation measures. The experime...

A user model that specifies user preferences on message handling is an essential component of an e-mail message categorizer. We present an approach that combines two learning algorithms, i.e. the Naïve Bayesian Classifier (NBC) and Progol, to model implicitly and explicitly reflected user preferences that may not be modeled by using either the algorithms alone. An experiment demonstrates the im...