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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. [ABSTRACT FROM AUTHOR]

A new system for selection of reference instances, which is called the EkP system (Exactly k Prototypes), has been introduced by us recently. In this paper we study suitability of the EkP method for training data reduction on seventeen datasets. As the underlaying classifier the well known IB1 system (1-Nearest Neighbor classifier) has been chosen. We compare generalization ability of our method to performance of IB1 trained on the entire training data and performance of LVQ, Learning Vector Quantization, for which the same number of codebooks has been chosen as the number of prototypes selected by the EkP system. The comparison indicates that even with only a few prototypes which have been chosen by the EkP method on nearly all seventeen datasets statistically indistinguishable results from those given by the IB1 system have been obtained. On many datasets generalization ability of the EkP method has been higher than the one attained with LVQ. [ABSTRACT FROM AUTHOR]

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Copyright of Polimery is the property of Industrial Chemistry Research Institute and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)