Cutting plane approach for efficient minimax risk classifiers in high-dimensional settings
High-dimensional data is common in multiple areas, such as health care and genomics, where the number of features can be hundreds of thousands. In such scenarios, the large number of features often lead to inefficient learning. Recently, methods based on constraint generation have enabled efficient learning of L1-regularized support vector machines (SVMs). In this work, we present efficient learning algorithm based on cutting plane approach for the recently proposed minimax risk classifiers (MRCs). The presented iterative algorithm obtains a sequence of MRCs with decreasing worst-case error probabilities while learning. Therefore, the algorithm can address the trade-off between training time and the classifier performance that can be suitable for the scenarios discussed above. In addition, the algorithm also provides a greedy feature selection as a side benefit.
Palabras clave: Supervised classification minimax classification high-dimensional learning cutting plane methods