Large-scale patent classification with min-max modular support vector machines

Patent classification is a large-scale, hierarchical, imbalanced, multi-label problem. The number of samples in a real-world patent classification typically exceeds one million, and this number increases every year. An effective patent classifier must be able to deal with this situation. This paper discusses the use of min-max modular support vector machine (M³-SVM) to deal with large-scale patent classification problems. The method includes three steps: decomposing a large-scale and imbalanced patent classification problem into a group of relatively smaller and more balanced two-class subproblems which are independent of each other, learning these subproblems using support vector machines (SVMs) in parallel, and combining all of the trained SVMs according to the minimization and the maximization rules. M³-SVM has two attractive features which are urgently needed to deal with large-scale patent classification problems. First, it can be realized in a massively parallel form. Second, it can be built up incrementally. Results from experiments using the NTCIR-5 patent data set, which contains more than two million patents, have confirmed these two attractive features, and demonstrate that M³-SVM outperforms conventional SVMs in terms of both training time and generalization performance.