Intellectual Approaches to Improvement of the Classification Decisions Quality on the Base of the SVM Classifier

Demidova, Liliya; Klyueva, Irina; Sokolova, Yulia; Stepanov, N.V.; Tyart, N.A.

doi:10.1016/j.procs.2017.01.070

Cited by 36 publications

(33 citation statements)

References 8 publications

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“…The SVM algorithm is successfully used for the different classification problems in various applications [8]. The SVM classifiers on the base of the SVM algorithm have been applied for credit risk analysis, medical diagnostics, text categorization, information extraction, etc [8].…”

Section: The Basic Principles Of the Svm Classifier Developmentmentioning

confidence: 99%

“…To develop the best SVM classifier it is necessary to find correctly the kernel function type, values of the kernel function parameters and value of the regularization parameter [8]. The solution of this problem can be achieved by the grid search of the kernel function types, values of the kernel function parameters and value of the regularization parameter that demands significant computational expenses.…”

Section: The Basic Principles Of the Svm Classifier Developmentmentioning

confidence: 99%

“…Quality of the SVM classifier can be measured by different classification quality indicators. There are the cross validation data indicator, the accuracy indicator, the classification completeness indicator and the ROC curve analysis based indicator, etc [8].…”

Section: The Basic Principles Of the Svm Classifier Developmentmentioning

confidence: 99%

“…Finding the separating hyperplane is basically the dual problem of searching a saddle point of the Lagrange function, which reduces to the problem of quadratic programming, containing only dual variables [8].…”

Section: The Basic Principles Of the Svm Classifier Developmentmentioning

confidence: 99%

“…, values of the kernel parameters and value of the regularization parameter C , which allows finding a compromise between maximizing of the gap separating the classes and minimizing of the total error [8].…”

Section: The Basic Principles Of the Svm Classifier Developmentmentioning

confidence: 99%

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Improving the Classification Quality of the SVM Classifier for the Imbalanced Datasets on the Base of Ideas the SMOTE Algorithm

Demidova

Klyueva

2017

ITM Web Conf.

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Abstract. The approach to the classification problem of the imbalanced datasets has been considered. The aim of this research is to determine the effectiveness of the SMOTE algorithm, when it is necessary to improve the classification quality of the SVM classifier, which is applied for classification of the imbalanced datasets. The experimental results which demonstrate the improvement of the SVM classifier quality with application of ideas the SMOTE algorithm for the imbalanced datasets in the sphere of medical diagnostics have been given.

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Section: The Basic Principles Of the Svm Classifier Developmentmentioning

confidence: 99%

Section: The Basic Principles Of the Svm Classifier Developmentmentioning

confidence: 99%

Section: The Basic Principles Of the Svm Classifier Developmentmentioning

confidence: 99%

Section: The Basic Principles Of the Svm Classifier Developmentmentioning

confidence: 99%

Section: The Basic Principles Of the Svm Classifier Developmentmentioning

confidence: 99%

See 3 more Smart Citations