# Does Support Vector Machine handle imbalanced Dataset?

Does SVM handles imbalanced dataset? Is that any parameters (like C, or misclassification cost) handling the imbalanced dataset?

where $\mathcal{P}$ and $\mathcal{N}$ represent the positive/negative training instances. In standard SVM we only have a single $C$ value, whereas now we have 2. The misclassification penalty for the minority class is chosen to be larger than that of the majority class.
Essentially this is equivalent to oversampling the minority class: for instance if $C_{pos} = 2 C_{neg}$ this is entirely equivalent to training a standard SVM with $C=C_{neg}$ after including every positive twice in the training set.