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? 
 A: For imbalanced data sets we typically change the misclassification penalty per class. This is called class-weighted SVM, which minimizes the following:
$$
\begin{align}
\min_{\mathbf{w},b,\xi} &\quad \sum_{i=1}^N\sum_{j=1}^N \alpha_i \alpha_j y_i y_j \kappa(\mathbf{x}_i,\mathbf{x}_j)  + C_{pos}\sum_{i\in \mathcal{P}} \xi_i + C_{neg}\sum_{i\in \mathcal{N}}\xi_i, \\
s.t. &\quad y_i\big(\sum_{j=1}^N \alpha_j y_j \kappa(\mathbf{x}_i, \mathbf{x}_j) + b\big) \geq 1-\xi_i,& i=1\ldots N \\
&\quad \xi_i \geq 0, & i=1\ldots N
\end{align}$$
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. 
This approach was introduced quite early, it is mentioned for instance in a 1997 paper:  

Edgar Osuna, Robert Freund, and Federico Girosi. Support Vector Machines: Training and Applications. Technical Report AIM-1602, 1997. (pdf)

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.
A: SVMs are able to deal with datasets with imbalanced class frequencies.  Many implementations allow you to have a different value for the slack penalty (C) for positive and negative classes (which is asymptotically equivalent to changing the class frequencies).  I would recommend setting the values of these parameters in order maximize generalization performance on a test set where the class frequencies are those you expect to see in operational use.
I was one of many people who wrote papers on this, here is mine, I'll see if I can find something more recent/better.  Try Veropoulos, Campbell and Cristianini (1999).
