Object oriented approach to combined learning of decision tree and ADF GP
Ayahiko Niimi, E. Tazaki · 2003
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.