A comparison study on multiple binary-class SVM methods for unilabel text categorization

Kumar, Manoj; Gopal, M.

doi:10.1016/j.patrec.2010.02.015

Cited by 44 publications

(20 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…SVM, which is one of the state-of-the-art pattern classifiers (Kumar & Gopal, 2010), is used as the classification algorithm. Also, the success measure is selected as well-known Micro-F1 score for this study.…”

Section: Classification Algorithm and Success Measurementioning

confidence: 99%

The impact of preprocessing on text classification

Uysal

Günal

2014

Information Processing & Management

512

234

View full text Add to dashboard Cite

Section: Classification Algorithm and Success Measurementioning

confidence: 99%

The impact of preprocessing on text classification

Uysal

Günal

2014

Information Processing & Management

512

234

View full text Add to dashboard Cite

“…The third and last classifier is a neural network (NN) classifier [4]. All those classification methods have been commonly used for text classification research in the literature and proven to be significantly successful [10,14,23,25,43].…”

Section: Classification Algorithmsmentioning

confidence: 99%

A novel probabilistic feature selection method for text classification

Uysal

Günal

2012

Knowledge-Based Systems

270

128

View full text Add to dashboard Cite

“…First we evaluate the fifth misclassified document in economy category. It is classified as in medical category (3) by SVM classifier even though it is in economy category (1). Title of this news document is "The ministry of health's objection towards the cord blood trade" and the document expresses the legal restrictions which have been applied due to limit the cord blood trade.…”

Section: Experimental Results and Analysismentioning

confidence: 99%

“…The most popular ones include regression models, probabilistic Bayesian models, decision trees, decision rule learners, K-nearest neighbors (KNN), computing with words, association rule mining and SVM. Among these methods, SVM achieves superior results in text classification and pattern recognition problems [1]. (Fabrizio Sebastiani, 2005) also emphasized SVM classifier in his review paper of text categorization because of its best performance in comparative text categorization experiments so far.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Document Categorization Approach for Turkish Based Texts

Omurca¹,

Baş²,

Ekinci³

2015

Int J Intell Syst Appl Eng

View full text Add to dashboard Cite

Since, it is infeasible to classify all the documents with human effort due to the rapid and uncontrollable growth in textual data, automatic methods have been approached in order to organize the data. Therefore a support vector machine (SVM) classifier is used for text categorization in this study. In text categorization applications, the text representation process could take a huge computation time on weighting the huge size of terms. So far, lexicons that contain less number of terms are used for the solution in the literature. However it has been observed that these kinds of solutions reduce the accuracy of the text classification. In this paper, the term-document matrix is constructed as user dependent according to the purpose of classification. Since the number of terms is still relatively large, we used a hash table for efficient search of terms. Hereby an efficient and rapid TF-IDF method is introduced to construct a weight-matrix to represent the term-document relations and a study concerning classification of the documents in Turkish based news and Turkish columnists is conducted. With the proposed study, the computational time that is required for term-weighting process is reduced substantially; also 99% accuracy is achieved in determination of the news categories and 98% accuracy is achieved in detection of the columnists.

show abstract

A comparison study on multiple binary-class SVM methods for unilabel text categorization

Cited by 44 publications

References 11 publications

The impact of preprocessing on text classification

The impact of preprocessing on text classification

A novel probabilistic feature selection method for text classification

An Efficient Document Categorization Approach for Turkish Based Texts

Contact Info

Product

Resources

About