Dealing with multiclass classification problem is still considered as significant hurdle to determine an efficient classifier. Moreover, this task is getting rough when it comes to imbalanced data, which defined as the number of some classes are much bigger than the others. This condition could cause the classifier tends to predict the majority class and ignore the minority class. This study proposed Synthetic Minority Oversampling Technique-Least Square Support Vector Machine (SMOTE-LSSVM) to build a classifier addressing this problem. Particle Swarm Optimization-Gravitational Search Algorithm (PSO-GSA) was used to optimize the parameters of LS-SVM, while SMOTE was employed to balance the data. The effectiveness of SMOTE-LSSVM was examined on malignancy of breast cancer dataset. Results of this studies showed that the accuracy rate after applying SMOTE increased significantly compare to the results without applying SMOTE.