AN OPTIMIZED SUPPORT VECTOR MACHINE WITH GENETIC ALGORITHM FOR IMBALANCED DATA CLASSIFICATION

In supervised machine learning, class imbalance is commonly occurring when the number of examples that represent one much lower than other classes. Since an data may generate suboptimal classification models, it could lead to minority are misclassified frequently and hardly achieving best performance. This study proposes improved support vector (SVM) method for imbalanced namely as SVM-GA by optimizing SVM algorithm with Genetic Algorithm (GA) over a synthetic oversampling technique. Besides considering sampling in optimized SVM, experimental result shows proposed improves 97% compared baseline model selected models. The had significant performance outperformed models based Grid search Randomized most cases, especially datasets which have extremely rare cases.