Fuzzy variable-branch decision tree

Shiueng-Bien Yang

doi:10.1117/1.3504357

1 October 2010 Fuzzy variable-branch decision tree

Shiueng-Bien Yang

Author Affiliations +

Journal of Electronic Imaging, Vol. 19, Issue 4, 043012 (October 2010). https://doi.org/10.1117/1.3504357

Abstract

Decision trees and their variants recently have been proposed. All trees used are fixed M-ary tree structured, such that the training samples in each node must be artificially divided into a fixed number of branches. This work proposes a fuzzy variable-branch decision tree (FVBDT) based on the fuzzy genetic algorithm (FGA). The FGA automatically searches for the proper number of branches of each node according to the classification error rate and the classification time of FVBDT. Therefore, FGA reduces both the classification error rate and classification time, and then optimizes the FVBDT. In our experiments, FVBDT outperforms the traditional C-fuzzy decision tree (CFDT) based on the fuzzy C-means (FCM) algorithm.

©(2010) Society of Photo-Optical Instrumentation Engineers (SPIE)

Citation Download Citation

Shiueng-Bien Yang "Fuzzy variable-branch decision tree," Journal of Electronic Imaging 19(4), 043012 (1 October 2010). https://doi.org/10.1117/1.3504357

Published: 1 October 2010

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available