Binary classification refers to supervised techniques splitting a set of points in two classes. Those algorithms acquire knowledge from a training set of points whose membership is known for each class. Binary classification plays a central role in the solution
of many scientific, financial, engineering, medical and biological problems. ReGEC, a new generalized eigenvalue classification method, gives results comparable to state of the art techniques, in terms
of classification accuracy. The advantage of this method relies in its lower computational complexity, with respect to the existing techniques based on generalized eigenvalue problems, and efficient implementation on distributed memory parallel computers.
M.R. Guarracino, C. Cifarelli, O. Seref, P. Pardalos.
A Classification Method Based on Generalized Eigenvalue Problems, Optimization Methods and Software, vol. 22, n. 1 pp. 73-81, 2007.
M. R. Guarracino, C. Cifarelli, O. Seref, P. M. Pardalos, A Parallel Classification Method for Genomic and Proteomic Problems, 20th International Conference on Advanced Information Networking and Applications - Volume 2 (AINA'06), 2006, pp. 588-592.