A Neural Tree Network Ensemble Mode for Disease Classification
A neural tree network ensemble model is proposed for classification which is an important research field in data mining and machine learning. Firstly, establishes each single neural tree network by using an improved hybrid breeder genetic programming, and then more neural tree networks are combined to form the final classification model by the idea of ensemble learning. Simulation results on two disease classification problems show that this model is effective for the classification, and has better performance in classification precision, feature selection and structure simplification, especially for classification with multi-class attributes.
KeywordsNeural tree network Breeder genetic programming Ensemble learning Disease classification
- 1.Han JW, Kamber M (2007) Data mining: concepts and techniques, 2nd edn. Machine Press, ChinaGoogle Scholar
- 2.Friedman N, Geiger D, Goldszmidt M (1997) Bayesian network classifiers. Mach Learn 29(2–3):131–163Google Scholar
- 8.Chen YH, Yang B, Dong J (2004) Evolving flexible neural networks using ant programming and PSO algorithm. In: International symposiums neural networks (ISNN’04), Dalian, China, LNCS3173, pp 211–216Google Scholar
- 11.Zhang BT, Muhlenbein H (1993) Genetic programming of minimal neural nets using Occam’s razor. In: Forrest S (ed) Proceeding of fifth international conference on genetic algorithms, Forum, vol 1. Morgan Kaufmann, pp 342–349Google Scholar
- 13.Muhlenbein H (1992) How genetic algorithms really work. Mutation and hill-climbing. In: Parallel problem solving from nature PPSN II, vol 1. North-Holland, pp 15–25Google Scholar