Improving a SVM Meta-classifier for Text Documents by using Naive Bayes

Morariu, Daniel; Vintan, Lucian; Cioran, E.

doi:10.15837/ijccc.2010.3.2487

Cited by 8 publications

(8 citation statements)

References 8 publications

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“…In our previous work [9] and [11] it is presented a meta-classifier, based on 8 SVM classifiers and one Bayes classifier that were used to improve the classification accuracy for text documents. In those works 3 meta-classifier models are used: majority vote, selection based on the Euclidian distance and selection based on the cosine angle.…”

Section: Meta-classifier Modelsmentioning

confidence: 99%

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Weights Space Exploration Using Genetic Algorithms for Meta-classifier in Text Document Classification

Cretulescu¹,

Morariu²,

Breazu³

et al. 2012

SIC

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Automatic document classification has become an important task because of the continually increasing number of text documents with the users have to deal with. The aim of this paper is to develop a non-adaptive meta-classifier for text documents that has an increased classification accuracy. The developed meta-classifier is based on combining some SVM classifiers and a Naïve Bayes classifier. We proposed a new meta-classification method which takes into consideration the corresponding positions and confidence degrees obtained for all the classes. In this work we have tried to find, using Genetic Algorithms, the optimal weighting factors for the values returned by each classifier separately. Consequently, it is possible for the meta-classifier to select as the winner class, a class that is not hierarchized as the first one by any of the compounded classifiers. The experimental results have showed that the classification accuracy can be improved through the proposed method.

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Section: Meta-classifier Modelsmentioning

confidence: 99%

“…All meta-classifiers presented in this article contain eight SVM type classifiers and one Naïve Bayes classifier [11].…”

Section: Meta-classifier Modelsmentioning

confidence: 99%

Weights Space Exploration Using Genetic Algorithms for Meta-classifier in Text Document Classification

Cretulescu¹,

Morariu²,

Breazu³

et al. 2012

SIC

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“…Third, SVM is built on the structural risk minimization principle rather than the empirical risk minimization principle, which can minimize the upper bound of the generalization error. Because of its outstanding performance, SVM has already reached many achievements in classification problems [4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

A sparse method for least squares twin support vector regression

Huang

Wei²,

Zhou

2016

Neurocomputing

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“…Therefore, compared with other machine learning methods, such as artificial neural network [2,3,4], SVM owns better generalization ability. Within a few years after its introduction SVM has played excellent performance on many real-world predictive data mining applications such as text categorization [5], time series prediction [6], pattern recognition [7] and image processing [8], etc.…”

Section: Introductionmentioning

confidence: 99%

Polynomial Smooth Twin Support Vector Machines

Ding¹,

Huang²,

Xu³

et al. 2014

Appl. Math. Inf. Sci.

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Smoothing functions can transform the unsmooth twin support vector machines (TWSVM) into smooth ones, and thus better classification results can be obtained. It has been one of the key problems to seek a better smoothing function in this field for a long time. In this paper, a novel version for smooth TWSVM, termed polynomial smooth twin support vector machines (PSTWSVM), is proposed. In PSTWSVM, using the series expansion, a new class of polynomial smoothing is proposed, and then their important properties are discussed. It is shown that the approximation accuracy and smoothness rank of polynomial functions can be as high as required. Subsequently, the polynomial functions are adopted to convert the original constrained quadratic programming problems of TWSVM into unconstrained minimization problems, and then are solved by the well-known Newton-Armijo algorithm. The effectiveness of the proposed method is demonstrated via experiments on synthetic and real-word benchmark datasets.

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Improving a SVM Meta-classifier for Text Documents by using Naive Bayes

Cited by 8 publications

References 8 publications

Weights Space Exploration Using Genetic Algorithms for Meta-classifier in Text Document Classification

Weights Space Exploration Using Genetic Algorithms for Meta-classifier in Text Document Classification

A sparse method for least squares twin support vector regression

Polynomial Smooth Twin Support Vector Machines

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