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Text classification deals with allocating a text document to a predetermined class. Generally, this involves learning about a class from representations of documents belonging to that class. In this paper, we propose a classifier combination that uses a Multinomial Naïve Bayesian (MNB) classifier along with Bayesian Networks (BN) classifier. The results of two classifiers are combined by taking an...
Text categorization is an important research direction of current information retrieval. The traditional text classification method use the support vector machine (SVM) and the Bayes classification algorithm (etc). On the basis of Rough Set on text categorization, this paper put forward the idea of variable precision rough set model for Chinese text categorization, which use the attribute reduct algorithm...
Text classification plays an important role in information extraction and summarization, text retrieval, and question-answering. The discriminative multinomial naive Bayes classifier has been a focus of research in the field of text classification. This paper increases the accuracy of discriminative multinomial Bayesian classifier with the usage of the feature selection technique that evaluates the...
In this paper, a general decision layer classification fusion model, based on information fusion for improving classification precision, is proposed, that is, different multi-classification algorithms as the feature layer doing respective classification, and the results of classification algorithms are input into decision level, the last classification result is output.This model is applied into improving...
In the text literature, many Bayesian generative models were proposed to represent documents and words in order to process text effectively and accurately. As the most popular one of these models, Latent Dirichlet Allocation Model(LDA) did great job in dimensionality reduction for document classification. In this paper, inspiring by latent Dirichlet allocation model, we propose LDCM or latent Dirichlet...
Document classification involves the act of classifying documents according to their content to predefined categories. One of the main problems of document classification is the large dimensionality of the data. To overcome this problem, feature selection is required which reduces the number of selected features and thus improves the classification accuracy. In this paper, a new algorithm for multi-label...
Due to the flood of pornographic web sites on the internet, effective Web filtering systems are essential. Web filtering based on content has become one of the important techniques to handle and filter inappropriate information on the web. We examine two machine learning algorithms (support vector machines and Naive Bayes) for pornographic web filtering based on text content. We then focus initially...
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