¹ Dept. of Computer Science and Engineering, Khulna University of Engineering & Technology (KUET), Bangladesh
² Dept. of Human and Artificial Intelligence Systems, University of Fukui, Japan

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Abstract—Ensembles with several neural networks are widely used to improve the generalization performance over a single network. A proper diversity among component networks is considered as an important parameter for ensemble construction so that the failure of one may be compensated by others. Data sampling, i.e., different training sets for different networks, is the most investigated technique for diversity than other approaches. This paper presents a data sampling based neural network ensemble method where the individual networks are trained on the union of original training set and a set of some artificially generated examples. Generated examples are different for different networks and are the element to produce diversity among the networks. After each network is trained, the method checks whether the trained network is suitable to ensemble or not, and absorbs the network based on the ensemble performance with it. The effectiveness of the method is evaluated on a suite of 20 benchmark classification problems. The experimental results show that the performance of this ensemble method is better or competitive with respect to the existing popular methods.

Index Terms—Artificial training example, diversity, generalization, neural network ensemble, bagging, AdaBoost, negative correlation learning.

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Cite: M. A. H. Akh, P. C. Shill, and K. Murase, " Ensembles of Artificial Example based Neural Networks," Journal of Computers vol. 5, no. 12, pp. 1819-1827, 2010.