- Tytuł:
- Fast reduction of large dataset for nearest neighbor classifier
- Autorzy:
- Raniszewski, M.
- Powiązania:
- https://bibliotekanauki.pl/articles/333106.pdf
- Data publikacji:
- 2010
- Wydawca:
- Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
- Tematy:
-
metody podziału
metody redukcji
przetwarzanie obrazów
reguła najbliższego sąsiada
pomiar reprezentatywny
division methods
reduction methods
images processing
nearest neighbour rule
representative measure - Opis:
- 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.
- Źródło:
-
Journal of Medical Informatics & Technologies; 2010, 16; 111-116
1642-6037 - Pojawia się w:
- Journal of Medical Informatics & Technologies
- Dostawca treści:
- Biblioteka Nauki
Artykuł