An Empirical Comparison of Text Categorization Methods

This paper explores the use of Support Vector Machines SVMs for learning text classi ers from examples. It analyzes the particular properties of learning with text data and identi es why SVMs are appropriate for this task. Empirical results support the theoretical ndings. SVMs achieve substantial improvements over the currently best performing methods and behave robustly ove r a v ariety of di erent learning tasks. Furthermore, they are fully automatic, eliminating the need for manual parameter tuning.

Artificial Intelligence Research, 2016

An extensive empirical evaluation of classifiers and feature selection methods for text categorization is presented. More than 500 models were trained and tested using different combinations of corpora, term weighting schemes, number of features, feature selection methods and classifiers. The performance measures used were micro-averaged F measure and classifier training time. The experiments used five benchmark corpora, three term weighting schemes, three feature selection methods and four classifiers. Results indicated only slight performance improvement with all the features over only 20% features selected using Information Gain and Chi Square. More importantly, this performance improvement was not deemed statistically significant. Support Vector Machine with linear kernel reigned supreme for text categorization tasks producing highest F measures and low training times even in the presence of high class skew. We found statistically significant difference between the performance of Support Vector Machine and other classifiers on text categorization problems.

2002

Journal of The American Society for Information Science and Technology, 2004

Text Categorization is the process of assigning documents to a set of previously fixed categories. A lot of research is going on with the goal of automating this time-consuming task. Several different algorithms have been applied, and Support Vector Machines (SVM) have shown very good results. In this report, we try to prove that a previous filtering of the words used by SVM in the classification can improve the overall performance. This hypothesis is systematically tested with three different measures of word relevance, on two different corpus (one of them considered in three different splits), and with both local and global vocabularies. The results show that filtering significantly improves the recall of the method, and that also has the effect of significantly improving the overall performance.

Applied Soft Computing, 2007

This paper presents a least square support vector machine (LS-SVM) that performs text classification of noisy document titles according to different predetermined categories. The system's potential is demonstrated with a corpus of 91,229 words from University of Denver's Penrose Library catalogue. The classification accuracy of the proposed LS-SVM based system is found to be over 99.9%. The final classifier is an LS-SVM array with Gaussian radial basis function (GRBF) kernel, which uses the coefficients generated by the latent semantic indexing algorithm for classification of the text titles. These coefficients are also used to generate the confidence factors for the inference engine that present the final decision of the entire classifier. The system is also compared with a K-nearest neighbor (KNN) and Naïve Bayes (NB) classifier and the comparison clearly claims that the proposed LS-SVM based architecture outperforms the KNN and NB based system. The comparison between the conventional linear SVM based classifiers and neural network based classifying agents shows that the LS-SVM with LSI based classifying agents improves text categorization performance significantly and holds a lot of potential for developing robust learning based agents for text classification. # 2006 Published by Elsevier B.V.

System Sciences, 2003. …, 2003

Text categorization is the process of sorting text documents into one or more predefined categories or classes of similar documents. Differences in the results of such categorization arise from the feature set chosen to base the association of a given document with a given category. Advocates of text categorization recognize that the sorting of text documents into categories of like

2002

International Journal of Computer Applications, 2013

The automated categorization (classification) of texts into predefined categories is one of the widely explored fields of research in text mining. Now-a-days, availability of digital data is very high, and to manage them in predefined categories has become a challenging task. Machine learning technique is an approach by which we can train automated classifier to classify the documents with minimum human assistance. This paper discusses the Naïve Bayes, Rocchio, k-Nearest Neighborhood and Support Vector Machine methods within machine learning paradigm for automated text categorization of given documents in predefined categories.

Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium, 2000

We propose to solve a text categorization task using a new metric between documents, based on a priori semantic knowledge about words. This metric can be incorporated into the definition of radial basis kernels of Support Vector Machines or directly used in a K-nearest neighbors algorithm. Both SVM and KNN are tested and compared on the 20newsgroups database. Support Vector Machines provide the best accuracy on test data.

2016

Perhaps the single largest data source in the world is the world wide web. Heterogeneous and unstructured nature of the data on web has challenged mining the web. Practical needs to extract textual information and unseen patterns continue to drive the research interest in text mining. Faultless categorization of texts can be better performed by machine learning techniques. In this paper we present a review of various text classification approaches under machine learning paradigm. Existing classification algorithms including Decision Tree, Naive Bayes, Support Vector Machine and k-Nearest Neighbors are compared based on speed, accuracy, interpretability and multi-class support.