A novel multi-classifier information fusion based on Dempster-Shafer theory: application to vibration-based fault detection

Achieving a high prediction rate is a crucial task in fault detection. Although various classification procedures are available, none of them can give high accuracy in all applications. Therefore, in this paper, a novel multi-classifier fusion approach is developed to boost the performance of the individual classifiers. This is acquired by using Dempster-Shafer theory (DST). However, in cases with conflicting evidences, the DST may give counter-intuitive results. In this regard, a preprocessing technique based on a new metric is devised in order to measure and mitigate the conflict between the evidences. To evaluate and validate the effectiveness of the proposed approach, the method is applied to 15 benchmarks datasets from UCI and KEEL. Further, it is applied for classifying polycrystalline Nickel alloy first-stage turbine blades based on their broadband vibrational response. Through statistical analysis with different noise levels, and by comparing with four state-of-the-art fusion techniques, it is shown that that the proposed method improves the classification accuracy and outperforms the individual classifiers.Comment: arXiv admin note: text overlap with arXiv:2007.0878

On The Cooperation Of Fuzzy Neural Networks Via A Coevolutionary Approach

This paper brings effort on the characterization of Cooperative Fuzzy Neural Networks (CFNNs). CFNNs encompass any conceptual or architectural aggregate in which two or more Fuzzy Neural Networks (FNNs) work cooperatively for the accomplishment of high-level objectives. In such context, the behavior of an FNN is, by some means, influenced by the behavior of its peers, and the performance of the whole group should contribute as complementary guidance for its individual training. A coevolutionary approach is presented as an auxiliary mechanism for the design and implementation of CFNNs. Implementation issues are described as a means to attest the applicability of the proposal.2785790Caminhas, W., Tavares, H., Gomide, F., Pedrycz, W., Fuzzy set based neural networks: Structure, learning and application (1999) Journal of Advanced Computational Intelligence, 3 (3), pp. 151-157Buckley, J., Hayashi, Y., Fuzzy neural networks (1994) Fuzzy Sets, Neural Networks and Soft Computing, pp. 233-249. , Van Nostrand Reinhold, New YorkIshigami, H., Fukuda, T., Shibata, T., Arai, F., Structure optimization of fuzzy neural network by genetic algorithm (1995) Fuzzy Sets and Systems, 71, pp. 257-264. , MayAliev, R., Fazlollahi, B., Vahidov, R., Genetic algorithm-based learning of fuzzy neural networks. Part 1: Feed-forward fuzzy neural networks (2001) Fuzzy Sets and Systems, 118 (2), pp. 351-358. , March(1999) Multiagent Systems: A Modern Approach to Distributed Artificial Intelligence, , G. Weiss (ed.)MIT Press, Cambridge, MassachusettsMaclin, R., Shavlik, J., Combining the predictions of multiple classifiers: Using competitive learning to initialize neural networks Proc. of the 14th International Joint Conference on Artificial Intelligence (IJCAI), 1995, pp. 524-531Huang, Y., Liu, K., Sue, C., The combination of multiple classifiers by a neural network approach (1995) Journal of Pattern Recognition and Artificial Intelligence, 9, pp. 579-597(1999) Combining Artificial Neural Nets: Ensemble and Modular Multi-Net Systems, , A. Scharkey (ed.)Springer, LondonPotter, M., De Jong, K., Cooperative coevolution: An architecture for evolving coadapted subcomponents (2000) Evolutionary Computation, 8 (1), pp. 1-29Puppala, N., Sen, S., Gordin, M., Shared memory based cooperative coevolution Proc. ICEC, 1998Burt, P.J., Hong, T.-H., Rosenfeld, A., Segmentation and estimation of image properties through cooperative hierarchical computation (1981) IEEE Transactions on Systems, Man, and Cybernetics, 11 (12)Coelho, A.L.V., Weingaertner, D., Von Zuben, F.J., Evolving heterogeneous neural networks for classification problems Proc. Genetic and Evolutionary Computation Conference (GECCO), San Francisco, USA, July 200