Improved adaptive splitting and selection: the hybrid training method of a classifier based on a feature space partitioning
PBN-AR
Instytucja
Wydział Elektroniki (Politechnika Wrocławska)
Informacje podstawowe
Główny język publikacji
eng
Czasopismo
International Journal of Neural Systems
ISSN
0129-0657
EISSN
Wydawca
DOI
URL
Rok publikacji
2014
Numer zeszytu
art. 1430007
Strony od-do
1-18
Numer tomu
vol. 24
Identyfikator DOI
Liczba arkuszy
Autorzy
Słowa kluczowe
pol
komitet klasyfikatorów
uczenie maszyn
klasyfikacja
Streszczenia
Język
eng
Treść
Currently, methods of combined classification are the focus of intense research. A properly designed group of combined classifiers exploiting knowledge gathered in a pool of elementary classifiers can successfully outperform a single classifier. There are two essential issues to consider when creating combined classifiers: how to establish the most comprehensive pool and how to design a fusion model that allows for taking full advantage of the collected knowledge. In this work, we address the issues and propose an AdaSS+, training algorithm dedicated for the compound classifier system that effectively exploits local specialization of the elementary classifiers. An effective training procedure consists of two phases. The first phase detects the classifier competencies and adjusts the respective fusion parameters. The second phase boosts classification accuracy by elevating the degree of local specialization. The quality of the proposed algorithms are evaluated on the basis of a wide range of computer experiments that show that AdaSS+ can outperform the original method and several reference classifiers.
Inne
System-identifier
000191324
CrossrefMetadata from Crossref logo
Cytowania
Liczba prac cytujących tę pracę
Brak danych
Referencje
Liczba prac cytowanych przez tę pracę
Brak danych