Persian Text Classification using naive Bayes algorithms and Support Vector Machine algorithm

One of the several benefits of text classification is to automatically assign document in predefined category is one of the primary steps toward knowledge extraction from the raw textual data. In such tasks, words are dealt with as a set of features. Due to high dimensionality and sparseness of feature vector results from traditional feature selection methods, most of the proposed text classification methods for this purpose lack performance and accuracy. Many algorithms have been implemented to the problem of Automatic Text Categorization that’s why, we tried to use new methods like Information Extraction, Natural Language Processing, and Machine Learning. This paper proposes an innovative approach to improve the classification performance of the Persian text. Naive Bayes classifiers which are widely used for text classification in machine learning are based on the conditional probability. we have compared the Gaussian, Multinomial and Bernoulli methods of naive Bayes algorithms with SVM algorithm. for statistical text representation, TF and TF-IDF and character-level 3 (3-Gram) [6,9] were used. Finally, experimental results on 10 newsgroups

Toward Optimal Feature Selection in Naive Bayes for Text Categorization

Automated feature selection is important for text categorization to reduce the feature size and to speed up the learning process of classifiers. In this paper, we present a novel and efficient feature selection framework based on the Information Theory, which aims to rank the features with their discriminative capacity for classification. We first revisit two information measures: Kullback-Leibler divergence and Jeffreys divergence for binary hypothesis testing, and analyze their asymptotic properties relating to type I and type II errors of a Bayesian classifier. We then introduce a new divergence measure, called Jeffreys-Multi-Hypothesis (JMH) divergence, to measure multi-distribution divergence for multi-class classification. Based on the JMH-divergence, we develop two efficient feature selection methods, termed maximum discrimination ($MD$) and $MD-\chi^2$ methods, for text categorization. The promising results of extensive experiments demonstrate the effectiveness of the proposed approaches.Comment: This paper has been submitted to the IEEE Trans. Knowledge and Data Engineering. 14 pages, 5 figure

Hierarchical classification for multiple, distributed web databases

The proliferation of online information resources increases the importance of effective and efficient distributed searching. Our research aims to provide an alternative hierarchical categorization and search capability based on a Bayesian network learning algorithm. Our proposed approach, which is grounded on automatic textual analysis of subject content of online web databases, attempts to address the database selection problem by first classifying web databases into a hierarchy of topic categories. The experimental results reported demonstrate that such a classification approach not only effectively reduces the class search space, but also helps to significantly improve the accuracy of classification performance

Using online linear classifiers to filter spam Emails

The performance of two online linear classifiers - the Perceptron and Littlestone’s Winnow – is explored for two anti-spam filtering benchmark corpora - PU1 and Ling-Spam. We study the performance for varying numbers of features, along with three different feature selection methods: Information Gain (IG), Document Frequency (DF) and Odds Ratio. The size of the training set and the number of training iterations are also investigated for both classifiers. The experimental results show that both the Perceptron and Winnow perform much better when using IG or DF than using Odds Ratio. It is further demonstrated that when using IG or DF, the classifiers are insensitive to the number of features and the number of training iterations, and not greatly sensitive to the size of training set. Winnow is shown to slightly outperform the Perceptron. It is also demonstrated that both of these online classifiers perform much better than a standard Naïve Bayes method. The theoretical and implementation computational complexity of these two classifiers are very low, and they are very easily adaptively updated. They outperform most of the published results, while being significantly easier to train and adapt. The analysis and promising experimental results indicate that the Perceptron and Winnow are two very competitive classifiers for anti-spam filtering