A problem arises in data mining, when classifying unbalanced datasets using Support Vector Machines. Because of the uneven distribution and the soft margin of the classifier, the algorithm tries to improve the general accuracy of classifying a dataset, and in this process it might misclassify a lot of weakly represented classes, confusing their class instances as overshoot values that appear in the dataset, and thus ignoring them. This paper introduces the Enhancer, a new algorithm that improves the Cost-sensitive classification for Support Vector Machines, by multiplying in the training step the instances of the underrepresented classes. We have discovered that by oversampling the instances of the class of interest, we are helping the Support Vector Machine algorithm to overcome the soft margin. As an effect, it classifies better future instances of this class of interest. Experimentally we have found out that our algorithm performs well on distributed databases.