By Topic

Object oriented approach to combined learning of decision tree and ADF GP

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Niimi, A. ; Dept. of Control & Syst. Eng., Toin Univ. of Yokohama, Japan ; Tazaki, Eiichiro

There are many learning methods for classification systems. Genetic programming (one of the methods) can change trees dynamically, but its learning speed is slow. Decision tree methods using C4.5 construct trees quickly, but the network may not classify correctly when the training data contains noise. For such problems, we proposed an object oriented approach, and a learning method that combines decision tree making method (C4.5) and genetic programming. To verify the validity of the proposed method we developed two different medical diagnostic systems. One is a medical diagnostic system for the occurrence of hypertension the other is for the meningoencephalitis. We compared the results of proposed method with prior ones

Published in:

Neural Networks, 1999. IJCNN '99. International Joint Conference on  (Volume:6 )

Date of Conference:

Jul 1999