Two ellipsoid Support Vector Machines

• Easy to implement.
• Works with any SVM library.
• Speeds up the SVM training process.
• Slightly increases classification results.
• Reduces number of support vectors.

In classification problems classes usually have different geometrical structure and therefore it seems natural for each class to have its own margin type. Existing methods using this principle lead to the construction of the different (from SVM) optimization problems. Although they outperform the standard model, they also prevent the utilization of existing SVM libraries. We propose an approach, named 2eSVM, which allows use of such method within the classical SVM framework.This enables to perform a detailed comparison with the standard SVM. It occurs that classes in the resulting feature space are geometrically easier to separate and the trained model has better generalization properties. Moreover, based on evaluation on standard datasets, 2eSVM brings considerable profit for the linear classification process in terms of training time and quality.We also construct the 2eSVM kernelization and perform the evaluation on the 5-HT2A ligand activity prediction problem (real, fingerprint based data from the cheminformatic domain) which shows increased classification quality, reduced training time as well as resulting model’s complexity.