(Universitas Sumatera Utara, Indonesia)(2) Opim Salim Sitompul
(Universitas Sumatera Utara, Indonesia)(3) Tulus Tulus
(Universitas Sumatera Utara, Indonesia)(4) Erna Budhiarti Nababan
(Universitas Sumatera Utara, Indonesia)*corresponding author
AbstractClass 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.
KeywordsClass Imbalance; Biased Support Vector Machine; Borderline-SMOTE; Positive Samples; Negative Samples
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DOIhttps://doi.org/10.26555/ijain.v4i1.146 |
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