New C-fuzzy decision tree with classified points

Shiueng-Bien Yang

doi:10.1117/1.2976421

1 July 2008 New C-fuzzy decision tree with classified points

Shiueng-Bien Yang

Author Affiliations +

Journal of Electronic Imaging, Vol. 17, Issue 3, 033017 (July 2008). https://doi.org/10.1117/1.2976421

Abstract

The C-fuzzy decision tree (CFDT)–based on the fuzzy C-means (FCM) algorithm has been proposed recently. In many experiments, the CFDT performs better than the “standard” decision tree, namely, the C4.5. A new C-fuzzy decision tree (NCFDT) is proposed, and it outperforms the CFDT. Two design issues for NCFDT are as follows. First, the growing method of NCFDT is based on both classification error rate and the average number of comparisons for the decision tree, whereas that of CFDT only addresses classification error rate. Thus, the proposed NCFDT performs better than the CFDT. Next, the classified point replaces the cluster center to classify the input vector in the NCFDT. The classified-points searching algorithm is proposed to search for one classified point in each cluster. The classification error rate of the NCFDT with classified points is smaller than that of CFDT with cluster centers. Furthermore, these classified points can be applied to the CFDT to reduce classification error rate. The performance of NCFDT is compared to CFDT and other methods in experiments.

©(2008) Society of Photo-Optical Instrumentation Engineers (SPIE)

Citation Download Citation

Shiueng-Bien Yang "New C-fuzzy decision tree with classified points," Journal of Electronic Imaging 17(3), 033017 (1 July 2008). https://doi.org/10.1117/1.2976421

Published: 1 July 2008

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