Double sort algorithm resulting in reference set of the desired size

• Autorzy: Raniszewski M.
• Języki publikacji: EN

Abstrakty

EN

An algorithm for obtaining the reduced reference set that does not exceed the desired size is presented. It consists in double sorting of the original reference set samples. The first sort key of the sample x is the number of such samples from the same class, that sample x is their nearest neighbour, while the second one is mutual distance measure proposed by Gowda and Krishna. The five medical datasets are used to compare the proposed procedure with the RMHC-P algorithm introduced by Skalak and the Gowda and Krishna algorithm, which are known as the most effective ones.

Słowa kluczowe

EN

reference set reduction; mutual distance measure; representative measure; Gowda and Krishna algorithm; Hart's algorithm; Skalak's RMHC-P algorithm; Double Sort Algorithm

PL

pomiar odległości wzajemnej; pomiar reprezentatywny; algorytm Gowdy-Krishny; podwójne sortowanie algorytmu

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2008
• Tom: Vol. 28, no. 4
• Strony: 43--50
• Opis fizyczny: Bibliogr. 9 poz., rys., tab.

Twórcy

autor

Raniszewski M.

• Technical University of Łódź, Computer Engineering Departament, ul. Stefanowskiego 18/22, 90-924 Łódź, Poland,

Bibliografia

• 1. Theodoridis S., Koutroumbas K.: Pattern Recognition - Third Edition. Academic Press - Elsevier, USA, 2006.
• 2. Duda R.O., Hart P.E., Stork D.G.: Pattern Classification - Second Edition. John Wiley & Sons, Inc, 2001.
• 3. Hart P.E.: The condensed nearest neighbor rule. IEEE Transactions on Information Theory, 1968, vol. IT-14, 3, 515-516.
• 4. Gowda K. C., Krishna G.: The condensed nearest neighbor rule using the concept of mutual nearest neighborhood. IEEE Transaction on Information Theory, 1979, v. IT-25, 4, 488-490.
• 5. Skalak D.B.: Prototype and feature selection by sampling and random mutation hill climbing algorithms. 11th International Conference on Machine Learning, New Brunswick, NJ, USA, 1994, 293-301.
• 6. Raniszewski M.: Reference set reduction algorithms based on double sorting. Computer Recognition Systems 2, Advances in Soft Computing, Springer Berlin/Heidelberg, 2007, 45, 258-265.
• 7. Asuncion A., Newman, D.J.: UCI Machine Learning Repository [http://www.ics.uci.edu/~mlearn/MLRepository.html]. Irvine, CA: University of California, School of Information and Computer Science, 2007.
• 8. Jóźwik A., Kieś P.: Reference set size reduction for 1-NN rule based on finding mutually nearest and mutually furthest pairs of points. Computer Recognition Systems, Advances in Soft Computing, Springer Berlin/Heidelberg, 2005, Vol. 30, 195-202.
• 9. Nakai K., Kanehisa M.: Expert System for Predicting Protein Localization Sites in Gram-Negative Bacteria. PROTEINS: Structure, Function, and Genetics 1991, 11, 95-110.