Fast reduction of large dataset for nearest neighbor classifier

• Autorzy: Raniszewski M.
• Języki publikacji: EN

Abstrakty

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.

Słowa kluczowe

EN

division methods; reduction methods; images processing; nearest neighbour rule; representative measure

PL

metody podziału; metody redukcji; przetwarzanie obrazów; reguła najbliższego sąsiada; pomiar reprezentatywny

• Wydawca: University of Silesia, Institute of Informatics, Computer Systems Department
• Czasopismo: Journal of Medical Informatics & Technologies
• Rocznik: 2010
• Tom: Vol. 16
• Strony: 111--116
• Opis fizyczny: Bibliogr. 9 poz., rys., tab.

Twórcy

autor

Raniszewski M.

• Computer Engineering Department, Technical University of Lodz, Stefanowskiego 18/22, Lodz, Poland

Bibliografia

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