Hybrid ensembles of decision trees and artificial neural networks
Kuo-Wei Hsu · 2012
Ensemble learning is inspired by the human group decision making process, and it has been found beneficial in various application domains. Decision tree and artificial neural network are two popular types of classification algorithms often used to construct classic ensembles. Recently, researchers proposed to use the mixture of both types to construct hybrid ensembles. However, researchers use decision trees and artificial neural networks together in an ensemble without further discussion. The focus of this paper is on the hybrid ensemble constructed by using decision trees and artificial neural networks simultaneously. The goal of this paper is not only to show that the hybrid ensemble can achieve comparable or even better classification performance, but also to provide an explanation of why it works.