Support vector machine (SVM) is one of the most popular algorithms in machine learning
and data mining. However, its reduced efficiency is usually observed for imbalanced
datasets. To improve the performance of SVM for binary imbalanced datasets, a new scheme
based on oversampling and the hybrid algorithm were introduced. Besides the use of a
single kernel function, SVM was applied with multiple kernel learning (MKL). A weighted
linear combination was defined based on the linear kernel function, radial basis function
(RBF kernel), and sigmoid kernel function for MKL. By generating the synthetic samples
in the minority class, searching the best choices of the SVM parameters and identifying
the weights of MKL by minimizing the objective function, the improved performance of
SVM was observed. To prove the strength of the proposed scheme, an experimental study,
including noisy borderline and real imbalanced datasets was conducted. SVM was applied
with linear kernel function, RBF kernel, sigmoid kernel function and MKL on all datasets.
The performance of SVM with all kernel functions was evaluated by using sensitivity,
G Mean, and F measure. A significantly improved performance of SVM with MKL was
observed by applying the proposed scheme.