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1

Fast reduction of large dataset for nearest neighbor classifier

Raniszewski M.

Journal of Medical Informatics & Technologies

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2010

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Vol. 16

111--116

EN

Accurate and fast classification of large data obtained from medical images is very important. Proper images (data) processing results to construct a classifier, which supports the work of doctors and can solve many medical problems. Unfortunately, Nearest Neighbor classifiers become inefficient and slow for large datasets. A dataset reduction is one of the most popular solution to this problem, but the large size of a dataset causes long time of a reduction phase for reduction algorithms. A simple method to overcome the large dataset reduction problem is a dataset division into smaller subsets. In this paper five different methods of large dataset division are considered. The received subsets are reduced by using an algorithm based on representative measure. The reduced subsets are combined to form the reduced dataset. The experiments were performed on a large (almost 82 000 samples) two–class dataset dating from ultrasound images of certain 3D objects found in a human body.

2

Bubble algorithm for the reduction of reference

Sierszeń A.

Journal of Medical Informatics & Technologies

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2010

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Vol. 16

117--123

3

The edited nearest neighbor rule based on the reduced reference set and the consistency criterion

Raniszewski M.

Biocybernetics and Biomedical Engineering

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2010

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Vol. 30, no. 1

31-40

EN

In this paper a new editing procedure for the Nearest Neighbor Rule (NN) is presented. The representativeness measure is introduced and used to choose the most representative samples of the classes. These samples constitute a reduced reference set. An edited reference set is created from all the training set samples (including samples from the reduced set), which are correctly classified by the NN rule operating with the reduced set. The performance of the presented method is evaluated and compared with five other well-known editing techniques, on five medical datasets.

4

Reduction of large reference sets with modified Chang's algorithm

Sierszeń A.

Automatyka / Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie

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2009

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T. 13, z. 3/1

1009-1019

EN

The advantage of the Chang's algorithm is a considerable reduction of the reference set. Its drawback is relatively small speed. The modification proposed by the author of this article aims at accelerating computations by replacing a larger number of objects, not only a pair of them, with one object. For any object in the reference set, it is possible to determine all objects from the same class which are located at a shorter distance to it than any other object from a different class. This group of objects can be replaced by a single artificial object.

PL

Zaletą algorytmu Changa jest znaczna redukcja zbioru odniesienia. Wadą tego algorytmu jest względnie mała szybkość działania. Modyfikacja zaproponowana przez autora niniejszego artykułu ma na celu przyspieszenie obliczeń poprzez zastępowanie jednym obiektem nie pary obiektów, ale większej liczby obiektów. Dla każdego obiektu ze zbioru odniesienia można wyznaczyć wszystkie obiekty z tej samej klasy znajdujące się od niego w mniejszej odległości niż jakikolwiek obiekt z innej klasy. Grupa takich obiektów może być zastąpiona jednym sztucznym obiektem.

5

Cascade algorithm for the reference set size reduction

Sierszeń A.

Automatyka / Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie

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2009

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T. 13, z. 3/1

995-1008

EN

Two algorithms of the reference set condensation, one of which is based on finding the mutually furthest points and the other is the modification of the Chang's algorithm, are respectively of the incremental and eliminative type, i.e. the size of the condensed set increases or is reduced as a result of a subsequent iteration. The combination of both aforementioned types of condensation, i.e. the cascade algorithm of condensation, is more effective than each of these algorithms executed sepa-rately.

PL

Dwa algorytmy kondesacji zbioru odniesienia, z których jeden jest oparty na znajdowaniu punktów wzajemnie najdalszych, a drugi jest modyfikacją algorytmu Changa, mają odpowiednio przyrostowy i eliminacyjnych charakter, tzn. w wyniku kolejnej iteracji wielkość skondensowanego zbioru odniesienia wzrasta lub jest redukowana. Kombinacja obu wymienionych typów kondensacji, tj. kaskadowy algorytm kondensacji, okazała się efektywniejsza od każdego z tych algorytmów działających samodzielnie.