Combining neural networks using the ranking figure of merit
Khaled A. Al-Ghoneim, B. V. K. Vijaya Kumar · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1996
The neural network community has recently shown a considerably interest in combining multiple neural networks (NNs). Such a combination usually improves the performance over a single NN because different NNs can complement each other. To achieve improved performance, the individual NNs must be trained independently. In this paper, three NNs are trained using the Ranking Figure of Merit objective function (with different parameters) that we introduced last year. We introduce a new method of combining NNs, which we call pooled objective function. The objective function is calculated for each NN and averaged to arrive at the pooled objective function. The combined vote is the class with the best pooled objective function. It is shown that the frequently used scheme of averaging the outputs is equivalent to the pooled mean squared error.