Scaling multi-class support vector machines using inter-class confusion

Shantanu Godbole, Sunita Sarawagi, Soumen Chakrabarti

Abstract: Support vector machines (SVMs) excel at two-class discriminative learning problems. They often outperform generative classifiers, especially those that use inaccurate generative models, such as the naïve Bayes (NB) classifier. On the other hand, generative classifiers have no trouble in handling an arbitrary number of classes efficiently, and NB classifiers train much faster than SVMs owing to their extreme simplicity. In contrast, SVMs handle multi-class problems by learning redundant yes/no (one-vs-others) classifiers for each class, further worsening the performance gap. We propose a new technique for multi-way classification which exploits the accuracy of SVMs and the speed of NB classifiers. We first use a NB classifier to quickly compute a confusion matrix, which is used to reduce the number and complexity of the two-class SVMs that are built in the second stage. During testing, we first get the prediction of a NB classifier and use that to selectively apply only a subset of the two-class SVMs. On standard benchmarks, our algorithm is 3 to 6 times faster than SVMs and yet matches or even exceeds their accuracy.