Abstract

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.