The PM-M prototype selection system

In this paper, the algorithm, realizing the author’s prototype selection method, called PM-M (Partial Memory - Minimization) is described in details. Computational experiments that have been carried out with the raw PM-M model and with its majority ensembles indicate that even for the system, for which the average size of the selected prototype sets constitutes only about five percent of the size of the original training datasets, the obtained results of classification are still in a good statistical agreement with the 1-Nearest Neighbor (IB1) model which has been trained on the original (i.e. unpruned) data. It has also been shown that the system under study is competitive in terms of generalization ability with respect to other well established prototype selection systems, such as, for example, CHC, SSMA and GGA. Moreover, the proposed algorithm has shown approximately one to three orders of magnitude decrement of time requirements with respect to the necessary time, needed to complete the calculations, relative to the reference prototype classifiers, taken for comparison. It has also been demonstrated that the PM-M system can be directly applied to analysis of very large data unlike most other prototype methods, which have to rely on the stratification approach.