Data mining is a technique to discover patterns and trends in data and can be used to create a model to predict those patterns and trends. This is particularly useful for data sets that are not amenable to traditional statistical analysis. One particular data mining task is classification, predicting a quantity that can only take on a finite number of values. An important class of binary classifiers are Support Vector Machines (SVMs). Traditional SVMs use constrained optimization to find a separating hyperplane. A new data point is classified based on which side of the separating hyperplane it happens to fall on. All SVMs try to minimize the number of potential errors the classifier will make by minimizing a sum of distances from the hyperplane. However, the actual task of classification does not place any importance on a distance. In order to model this more closely, we propose the Integer Support Vector Machine Classifier (ISVM). ISVM uses binary indicator error variables to directly minimize the number of potential errors the classifier can make.