Autor:

Kantavat, Pittipol ; Kijsirikul, Boonserm ; Songsiri, Patoomsiri ; Fukui, Ken-Ichi ; Numao, Masayuki

Współtwórca:

Korbicz, Józef (1951- ) - red. ; Uciński, Dariusz - red.

Tytuł:

Efficient decision trees for multi-class support vector machines using entropy and generalization error estimation

Tytuł publikacji grupowej:

AMCS, volume 28 (2018)

Temat i słowa kluczowe:

support vector machines ; multi-class classification ; generalization error ; entropy ; decision trees

Abstract:

We propose new methods for support vector machines using a tree architecture for multi-class classification. In each node of the tree, we select an appropriate binary classifier, using entropy and generalization error estimation, then group the examples into positive and negative classes based on the selected classifier, and train a new classifier for use in the classification phase. The proposed methods can work in time complexity between O(log2 N) and O(N), where N is the number of classes. ; We compare the performance of our methods with traditional techniques on the UCI machine learning repository using 10-fold cross-validation. The experimental results show that the methods are very useful for problems that need fast classification time or those with a large number of classes, since the proposed methods run much faster than the traditional techniques but still provide comparable accuracy.

Wydawca:

Zielona Góra: Uniwersytet Zielonogórski

Data wydania:

2018

Typ zasobu:

artykuł

DOI:

10.2478/amcs-2018-0054

Strony:

705-717

Źródło:

AMCS, volume 28, number 4 (2018) ; kliknij tutaj, żeby przejść

Jezyk:

eng

Licencja CC BY 4.0:

kliknij tutaj, żeby przejść

Prawa do dysponowania publikacją:

Biblioteka Uniwersytetu Zielonogórskiego