A comprehensive comparative study on term weighting schemes for text categorization with support vector machines

Lan, Man; Tan, Chik How; Low, Hwee-Boon; Sung, Sam-Yuan

doi:10.1145/1062745.1062854

Cited by 115 publications

(77 citation statements)

References 6 publications

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“…For example, as shown in [23,26], in text categorization using support vector machine (SVM), choosing an appropriate term weighting scheme is far more important than tuning kernel functions of SVM. See similar comments in [37] for the work on Naive Bayes text classifier.…”

Section: Heavy-tail and Term Weightingmentioning

confidence: 99%

Very sparse random projections

Hastie

Church

2006

Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

514

437

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There has been considerable interest in random projections, an approximate algorithm for estimating distances between pairs of points in a high-dimensional vector space. Let A ∈ R n×D be our n points in D dimensions. The method multiplies A by a random matrix R ∈ R D×k , reducing the D dimensions down to just k for speeding up the computation. R typically consists of entries of standard normal N (0, 1). It is well known that random projections preserve pairwise distances (in the expectation). Achlioptas proposed sparse random projections by replacing the N (0, 1) entries in R with entries in {−1, 0, 1} with probabilities { }, achieving a threefold speedup in processing time.We recommend using R of entries in {−1, 0, 1} with probabilities {} for achieving a significant √ Dfold speedup, with little loss in accuracy.

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Section: Heavy-tail and Term Weightingmentioning

confidence: 99%

Very sparse random projections

Hastie

Church

2006

Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

514

437

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“…Text categorization is a long-term research topic which has been actively studied in the communities of Web data mining, information retrieval and statistical learning [15,35]. Essentially the text categorization techniques have been the key toward automated categorization of large-scale Web pages and Web sites [18,27], which is further applied to improve Web searching engines in finding relevant documents and facilitate users in browsing Web pages or Web sites.…”

Section: Related Workmentioning

confidence: 99%

Large-scale text categorization by batch mode active learning

Hoi

Jin

Lyu

2006

Proceedings of the 15th International Conference on World Wide Web

188

101

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Large-scale text categorization is an important research topic for Web data mining. One of the challenges in large-scale text categorization is how to reduce the human efforts in labeling text documents for building reliable classification models. In the past, there have been many studies on applying active learning methods to automatic text categorization, which try to select the most informative documents for manually labeling. Most of these studies focused on selecting a single unlabeled document in each iteration. As a result, the text categorization model has to be retrained after each labeled document is solicited. In this paper, we present a novel active learning algorithm that selects a batch of text documents for manually labeling in each iteration. The key of the batch mode active learning is how to reduce the redundancy among the selected examples such that each example provides unique information for model updating. To this end, we use the Fisher information matrix as the measurement of model uncertainty and choose the set of documents that can efficiently minimize the Fisher information matrix of a classification model. Extensive experiments with three different datasets have shown that our algorithm is more effective than the state-of-the-art active learning techniques for text categorization and can be a promising tool toward large-scale text categorization on World Wide Web.

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“…Term weighting method for which information related to the category of document is available is supervised term weighting Method where as the traditional term weighting Binary, tf, tf.idf and its variant belongs to unsupervised term weighting method. This paper Compares the result on BPNN for Unsupervised Term Weighting Method tf, with new supervised Term weighting method tf.rf proposed by Man Lan,Chew-Lim Tan And Hwee-Boon Low [12] ,Section 2 discusses various steps of preprocessing, section 3 discusses the approaches of term weighting method, section 4 describes the BPNN algorithm, section 5 presents experiments performed and reports result.…”

Section: Introductionmentioning

confidence: 99%

Comparative Study and Analysis of Supervised and Unsupervised Term Weighting Methods on Text Classification

Motwani¹,

Tiwari²

2013

IJCA

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Text Classification is one of the booming area in research with the availability of huge amount of electronic data in the form of news article, research articles, email message, blog, web pages etc. Text Representation is a vital step for text classification. In text representation, term weighting method assigns appropriate weights to the term to get better performance; the term weighting method which uses known information on membership of training document is supervised Term weighting method. Unsupervised term weighting method tf is compared with supervised Term weighting method tf.rf with Back Propagation Neural Network, results of experiment demonstrates that term weighing method (tf.rf) performs better than (tf) term frequency.

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A comprehensive comparative study on term weighting schemes for text categorization with support vector machines

Cited by 115 publications

References 6 publications

Very sparse random projections

Very sparse random projections

Large-scale text categorization by batch mode active learning

Comparative Study and Analysis of Supervised and Unsupervised Term Weighting Methods on Text Classification

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