Reduction of reference set with the method of cutting hyperplanes

• Autorzy: Sierszeń A.
• Języki publikacji: EN

Abstrakty

EN

Reduction of this type may help to solve one of the greatest problems in pattern recognition, i.e. the compromise between the time of making a decision and its correctness. In the analysis of biomedical data, classification time is less important than certainty that classification is correct, i.e. that reliability of classification is accepted by the algorithm’s operator. It is usually possible to reduce the number of wrong decisions, using a more complex recognition algorithm and, as a consequence, increasing classification time. However, with a large quantity of data, this time may be considerably reduced by condensation of a set. Condensation of a set presented in this article is incremental, i.e. formation of the condensed reference set begins from a set containing one element. In each step, the size of the set is increased with one object. This algorithm consists in dividing the feature space with hyperplanes determined with pairs of the mutually furthest points. The hyperplanes are orthogonal to segments linking pairs of the mutually furthest points and they go through their centre.

Słowa kluczowe

EN

pattern recognition; nearest neighbour rule; reference set condensation; reference set reduction; method of cutting hyperplanes

PL

reguła NN

• Wydawca: University of Silesia, Institute of Informatics, Computer Systems Department
• Czasopismo: Journal of Medical Informatics & Technologies
• Rocznik: 2009
• Tom: Vol. 13
• Strony: 215--220
• Opis fizyczny: Bibliogr. 2 poz., rys., tab.

Twórcy

autor

Sierszeń A.

• Katedra Informatyki Stosowanej, Politechnika Łódzka, 90-924 Łódź, ul. Stefanowskiego 18/22

Bibliografia

• [1] ASUNCION A., NEWMAN D.J., UCI Machine Learning Repository, Irvine, CA: University of California, School of Information and Computer Science, 2007.
• [2] 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, Vol. 30, pp.195-202 Springer Berlin/Heidelberg, 2005.