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

Author

Niimi, Ayahiko ; Tazaki, Eiichiro

Author_Institution

Dept. of Control & Syst. Eng., Toin Univ. of Yokohama, Japan

Volume

6

fYear

1999

fDate

36342

Firstpage

4166

Abstract

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

Keywords

decision trees; genetic algorithms; learning (artificial intelligence); medical diagnostic computing; neural nets; noise; object-oriented methods; pattern classification; ADF GP learning; C4.5; automatic function definition; classification systems; combined learning; decision tree learning; genetic programming; hypertension; medical diagnostic systems; meningoencephalitis; noise; object oriented approach; Classification tree analysis; Control systems; Decision trees; Genetic programming; Hypertension; Learning systems; Medical diagnosis; Neural networks; Systems engineering and theory; Training data;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 1999. IJCNN '99. International Joint Conference on

Conference_Location

Washington, DC

ISSN

1098-7576

Print_ISBN

0-7803-5529-6

Type

conf

DOI

10.1109/IJCNN.1999.830832

Filename

830832