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Methods of strong reduction and edition of a reference set for the nearest neighbour rule

Raniszewski M.

Zeszyty Naukowe. Elektryka / Politechnika Łódzka

2010

z. 122

37-46

The article summarises a doctoral dissertation proposing new methods of a reference set reduction and edition for the Nearest Neighbour Rule (NN).The presented methods are designed to accelerate NN and to improve its classification quality. The algorithms use the concept of the object representativeness. The obtained results were compared with the results provided by well-known and popular reduction and editing procedures.

W artykule zaprezentowano tezy i podstawowe wyniki rozprawy doktorskiej dotyczącej nowych metod redukcji i edycji zbioru odniesienia dla reguły typu najbliszy sąsiad (NN). Przedstawione metody mają na celu przyspieszenie działania reguły NN i poprawę jej jakości klasyfikacji. Zaprezentowane algorytmy w większości wykorzystują pojęcie reprezentatywności obiektu. Wyniki ich działania zostały porównane z wynikami działania innych popularnych algorytmów redukcji i edycji.

Reduction of reference set with the method of cutting hyperplanes

Sierszeń A.

Journal of Medical Informatics & Technologies

2009

Vol. 13

215--220

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.