Neural Networks Fusion for Regression Problems
Ali Shamsoddini, John C. Trinder · International Journal of Machine Learning and Computing · 2012
A common solution to improving the generalization problem and increasing the efficiency of different ANNs is to use ANN ensembles.These methods focus on the possibility of generating different neural nets for a dataset and combining the results for acquiring a more accurate regression.In this paper, a new ensemble method called machine learner fusion-regression (MLF-R) is proposed to increase the accuracy of the results through focusing on difficult samples.The architecture of MLF-R includes two different parts: the first is a training phase from which final nets are selected after a filtering process; the second part is a weighted decision maker including a backpropagation structure which fuses the different nets derived from the first step to predict the outputs.The results demonstrate MLF-R is more efficient than bagging, different boosting methods and the implementation of single ANN methods with 18% to 51% higher accuracy.Moreover, MLF-R offers more stable results compared to the other methods which have been tested in this paper.