Two ellipsoid Support Vector Machines

• Published in: Expert systems with applications Vol. 41; no. 18; pp. 8211 - 8224
• Main Authors: Czarnecki, Wojciech Marian, Tabor, Jacek
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier Ltd 15.12.2014; Elsevier
• Subjects: Algorithmics. Computability. Computer arithmetics; Applied sciences; Chemistry; Classification; Computer science; control theory; systems; Construction; Data preprocessing; Data processing. List processing. Character string processing; Ellipsoids; Exact sciences and technology; Expert systems; General and physical chemistry; General. Nomenclature, chemical documentation, computer chemistry; Mahalanobis distance; Mathematical models; Mathematics; Memory organisation. Data processing; Multivariate analysis; Probability and statistics; Sciences and techniques of general use; Software; Statistics; Support Vector Machines; Theoretical computing; Theory of reactions, general kinetics. Catalysis. Nomenclature, chemical documentation, computer chemistry; Training; Data preprocessing; Support Vector Machines; Mahalanobis distance; Classification; Data analysis; Statistical analysis; Multiple machine; Multivariate analysis; Generalization; Properties; Computational complexity; Modeling; Standards; Data reduction; Geometrical structure; Computational chemistry; Linear process; Vector support machine; Profit; Metric; Ellipsoid; Computer security; Mathematical programming
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2014.07.015

•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.