Abstract
A framework for optimizing Fuzzy ARTMAP (FAM) neural networks using Genetic Algorithms (GAs) is proposed in this paper. A number of variables were identified for optimization, which include the presentation order of training data during the learning step, the feature subset selection of the training data, and the internal parameters of the FAM such as baseline vigilance and match tracking. A single configuration of all three variables were encoded as a chromosome string and evaluated by creating and training the FAM according to the variables. The fitness of the chromosome is determined by the final classification accuracy of the FAM. Evaluation on benchmark data sets are conducted with the results compared with literature. Experimental results indicate the effectiveness of the proposed framework in undertaking data classification tasks.
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Loghmanian, S.M.R., Jamaluddin, H., Ahmad, R., Yusof, R., Khalid, M.: Structure optimization of neural network for dynamic system modeling using multi-objective genetic algorithm. Neural Comput. Appl. 21(6), 1281–1295 (2012)
Chou, C.W., Chien, C.F., Gen, M.: A multiobjective hybrid genetic algorithm for TFT-LCD module assembly scheduling. IEEE Trans. Autom. Sci. Eng. 11(3), 692–705 (2014)
Carrano, E.G., Soares, L.A., Takahashi, R.H., Saldanha, R.R., Neto, O.M.: Electric distribution network multiobjective design using a problem-specific genetic algorithm. IEEE Trans. Power Delivery 21(2), 995–1005 (2006)
Celli, G., Ghiani, E., Mocci, S., Pilo, F.: A multiobjective evolutionary algorithm for the sizing and siting of distributed generation. IEEE Trans. Power Syst. 20(2), 750–757 (2005)
Tsai, J.T., Liu, T.K., Chou, J.H.: Hybrid Taguchi-genetic algorithm for global numerical optimization. IEEE Trans. Evol. Comput. 8(4), 365–377 (2004)
Tu, Z., Lu, Y.: A robust stochastic genetic algorithm (StGA) for global numerical optimization. IEEE Trans. Evol. Comput. 8(5), 456–470 (2004)
Carpenter, G.A., Grossberg, S., Marzukon, N., Reynolds, J.H., Rosen, D.B.: Fuzzy ARTMAP: a neural network architecture for incremental supervised learning of analog multidimensional maps. IEEE Trans. Neural Netw. 3(5), 698–713 (1992)
Williamson, J.R.: Gaussian ARTMAP: a neural network for fast incremental learning of noisy multidimensional maps. Neural Netw. 9(5), 881–897 (1996)
Carpenter, G.A., Gaddam, S.C.: Biased ART: a neural architecture that shifts attention toward previously disregarded features following an incorrect prediction. Neural Netw. 23(3), 435–451 (2010)
Dagher, I., Georgiopoulos, M., Heileman, G.L., Bebis, G.: An ordering algorithm for pattern presentation in fuzzy ARTMAP that tends to improve generalization performance. IEEE Trans. Neural Netw. 10(4), 768–778 (1999)
Frank, A., Asuncion, A.: UCI machine learning repository (2011). http://archive.ics.uci.edu/ml. Accessed March 2015
Ordóñez, F.J., Ledezma, A., Sanchis, A.: Genetic approach for optimizing ensembles of classifiers. In: FLAIRS Conference, pp. 89–94 (2008)
Woloszynski, T., Kurzynski, M., Podsiadlo, P., Stachowiak, G.W.: A measure of competence based on random classification for dynamic ensemble selection. Inf. Fusion 13(3), 207–213 (2012)
Yaghini, M., Shadmani, M.A.: GOFAM: a hybrid neural network classifier combining fuzzy ARTMAP and genetic algorithm. Artif. Intell. Rev. 39(3), 183–193 (2013)
Wei, H., Lin, X., Xu, X., Li, L., Zhang, W., Wang, X.: A novel ensemble classifier based on multiple diverse classification methods. In: 2014 11th International Conference on Fuzzy Systems and Knowledge Discovery, pp. 301–305 (2014)
Acknowledgments
This research is supported by Collaborative Research in Engineering, Science Technology (CREST) Grant P05C2-14 and University of Malaya Grant UM.C/625/1/HIR/MOHE/FCSIT/10.
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Seera, M., Liew, W.S., Loo, C.K. (2015). A Hybrid Model of Fuzzy ARTMAP and the Genetic Algorithm for Data Classification. In: Arik, S., Huang, T., Lai, W., Liu, Q. (eds) Neural Information Processing. ICONIP 2015. Lecture Notes in Computer Science(), vol 9490. Springer, Cham. https://doi.org/10.1007/978-3-319-26535-3_40
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DOI: https://doi.org/10.1007/978-3-319-26535-3_40
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