Biased support vector machine and weighted-smote in handling class imbalance problem

International Journal of Advances in Intelligent Informatics

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Field Value
 
Title Biased support vector machine and weighted-smote in handling class imbalance problem
 
Creator Hartono, Hartono
Sitompul, Opim Salim
Tulus, Tulus
Nababan, Erna Budhiarti
 
Subject Class Imbalance; Biased Support Vector Machine; Borderline-SMOTE; Positive Samples; Negative Samples
 
Description Class imbalance occurs when instances in a class are much higher than in other classes. This machine learning major problem can affect the predicted accuracy. Support Vector Machine (SVM) is robust and precise method in handling class imbalance problem but weak in the bias data distribution, Biased Support Vector Machine (BSVM) became popular choice to solve the problem. BSVM provide better control sensitivity yet lack accuracy compared to general SVM. This study proposes the integration of BSVM and SMOTEBoost to handle class imbalance problem. Non Support Vector (NSV) sets from negative samples and Support Vector (SV) sets from positive samples will undergo a Weighted-SMOTE process. The results indicate that implementation of Biased Support Vector Machine and Weighted-SMOTE achieve better accuracy and sensitivity.
 
Publisher Universitas Ahmad Dahlan
 
Contributor
 
Date 2018-03-31
 
Type info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion

 
Format application/pdf
 
Identifier http://ijain.org/index.php/IJAIN/article/view/146
10.26555/ijain.v4i1.146
 
Source International Journal of Advances in Intelligent Informatics; Vol 4, No 1 (2018): March 2018; 21-27
2548-3161
2442-6571
 
Language eng
 
Relation http://ijain.org/index.php/IJAIN/article/view/146/ijain_v4i1_p21-27
 
Rights Copyright (c) 2018 International Journal of Advances in Intelligent Informatics
https://creativecommons.org/licenses/by-sa/4.0
 

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