Combining supervised term-weighting metrics for SVM text classification with extended term representation

Haddoud, Mounia; Mokhtari, A.; Lecroq, Thierry; Abdeddaïm, Saïd

doi:10.1007/s10115-016-0924-1

Cited by 76 publications

(29 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The Vector Space Model (VSM) expresses text as a single vector in the space of high dimension feature words (see, e.g. Chen, 2016;Haddoud, 2016;Junejo, 2016). Each vector dimension represents the weight of the corresponding word that has been marked and sorted in the dictionary in the text, i.e.…”

Section: Text Classification Problemsmentioning

confidence: 99%

A method of dimensionality reduction by selection of components in principal component analysis for text classification

Yang-wu

Zong

2018

Filomat

View full text Add to dashboard Cite

Dimensionality reduction, including feature extraction and selection, is one of the key points for text classification. In this paper, we propose a mixed method of dimensionality reduction constructed by principal components analysis and the selection of components. Principal components analysis is a method of feature extraction. Not all of the components in principal component analysis contribute to classification, because PCA objective is not a form of discriminant analysis (see, e.g. . In this context, we present a function of components selection, which returns the useful components for classification by the indicators of the performances on the different subsets of the components. Compared to traditional methods of feature selection, SVM classifiers trained on selected components show improved classification performance and a reduction in computational overhead.

show abstract

Section: Text Classification Problemsmentioning

confidence: 99%

A method of dimensionality reduction by selection of components in principal component analysis for text classification

Yang-wu

Zong

2018

Filomat

View full text Add to dashboard Cite

show abstract

“…Após a distribuição das postagens, foram realizados dois experimentos utilizando a base de dados preparada, um com a abordagem proposta e outro utilizando um classificador Support Vector Machine (SVM) utilizando todas as palavras como atributos. Este algoritmo foi escolhido devido aos bons resultados alcançados no domínio de classificação de textos [Haddoud et al 2016]. O resultado de cada experimento foi comparado com o da avaliação manual para, em seguida, verificar qual o classificador obteve melhor desempenho.…”

Section: Avaliaçãounclassified

Mineração de Texto Aplicada à Identificação de Colaboração em Fóruns Educacionais

Dionísio¹,

Ferreira²,

Cavalcanti³

et al. 2017

Anais Do XXVIII Simpósio Brasileiro De Informática Na Educação (SBIE 2017)

View full text Add to dashboard Cite

Abstract. Educational forum is an important resource for interaction between teachers and students in virtual learning IntroduçãoO surgimento da modalidade de ensino a distância facilitou o acesso à educação a diferentes públicos, devido à flexibilidade em relação aos horários de estudo e acessibilidade. Por isso, a educação à distância (EAD) tem contribuído para a democratização do acesso à educação, principalmente, dos níveis técnico e superior. Por outro lado, o número reduzido de encontros presenciais pode desencadear a sensação de isolamento no estudante impactando negativamente na aprendizagem ou, até mesmo, levando à evasão [Daudt e Behar 2013].Nesse contexto, os Ambientes Virtuais de Aprendizagem (AVA) são utilizados como forma de possibilitar interações entre estudantes e professores. Sendo o fórum educacional, uma das ferramentas integradas aos AVA, capaz oportunizar colaboração entre os envolvidos, por meio de discussões assíncronas, posicionando se como um

show abstract

“…Support Vector Machine (SVM) is a classification technique based on the idea of Structural Risk Minimization (SRM) [1]. The algorithm has been used in many applications such as text classification [2,3], image classification [4,5], and bioinformatics [6,7,8]. The central ides of SVM is to find the optimal separating hyperplane between the positive and negative samples.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy least squares twin support vector machines

Sartakhti

Afrabandpey

Ghadiri

2019

Engineering Applications of Artificial Intelligence

View full text Add to dashboard Cite

Least Squares Twin Support Vector Machine (LST-SVM) has been shown to be an efficient and fast algorithm for binary classification. It combines the operating principles of Least Squares SVM (LS-SVM) and Twin SVM (T-SVM); it constructs two non-parallel hyperplanes (as in T-SVM) by solving two systems of linear equations (as in LS-SVM). Despite its efficiency, LST-SVM is still unable to cope with two features of real-world problems. First, in many real-world applications, labels of samples are not deterministic; they come naturally with their associated membership degrees. Second, samples in real-world applications may not be equally important and their importance degrees affect the classification. In this paper, we propose Fuzzy LST-SVM (FLST-SVM) to deal with these two characteristics of real-world data. Two models are introduced for FLST-SVM: the first model builds up crisp hyperplanes using training samples and their corresponding membership degrees. The second model, on the other hand, constructs fuzzy hyperplanes using training samples and their membership degrees. Numerical evaluation of the proposed method with synthetic and real datasets demonstrate significant improvement in the classification accuracy of FLST-SVM when compared to well-known existing versions of SVM.

show abstract

Combining supervised term-weighting metrics for SVM text classification with extended term representation

Cited by 76 publications

References 26 publications

A method of dimensionality reduction by selection of components in principal component analysis for text classification

A method of dimensionality reduction by selection of components in principal component analysis for text classification

Mineração de Texto Aplicada à Identificação de Colaboração em Fóruns Educacionais

Fuzzy least squares twin support vector machines

Contact Info

Product

Resources

About