Low false positive learning with support vector machines

• Novel 2-level classification method for low false positive classification.
• Level 1 defines a decision boundary through an SVM classifier.
• Level 2 defines a sensitive area around the decision boundary.
• The sensitive area are analyzed by a second classifier to control false positives.
• Method’s effectiveness showed trough comparisons to other solutions in 33 datasets.

Most machine learning systems for binary classification are trained using algorithms that maximize the accuracy and assume that false positives and false negatives are equally bad. However, in many applications, these two types of errors may have very different costs. In this paper, we consider the problem of controlling the false positive rate on SVMs, since its traditional formulation does not offer such assurance. To solve this problem, we define a feature space sensitive area, where the probability of having false positives is higher, and use a second classifier (unanimity k-NN) in this area to better filter errors and improve the decision-making process. We call this method Risk Area SVM (RA-SVM). We compare the RA-SVM to other state-of-the-art methods for low false positive classification using 33 standard datasets in the literature. The solution we propose shows better performance in the vast majority of the cases using the standard Neyman–Pearson measure.