Abstract
Probabilistic Neural Networks (PNNs) constitute a promising methodology for classification and prediction tasks. Their performance depends heavily on several factors, such as their spread parameters, kernels, and prior probabilities. Recently, Evolutionary Bayesian PNNs were proposed to address this problem by incorporating Bayesian models for estimation of spread parameters, as well as Particle Swarm Optimization (PSO) as a means to select prior probabilities. We further extend this class of models by introducing new features, such as the Epanechnikov kernels as an alternative to the Gaussian ones, and PSO for parameter configuration of the Bayesian model. Experimental results of five extended models on widely used benchmark problems suggest that the proposed approaches are significantly faster than the established ones, while exhibiting competitive classification accuracy.
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References
Specht, D.F.: Probabilistic neural networks. Neural Networks 2, 109–118 (1990)
Guo, J., Lin, Y., Sun, Z.: A novel method for protein subcellular localization based on boosting and probabilistic neural network. In: 2nd Asia-Pacific Bioinf. Conf. (APBC 2004), Dunedin, New Zealand, pp. 20–27 (2004)
Holmes, E., Nicholson, J.K., Tranter, G.: Metabonomic characterization of genetic variations in toxicological and metabolic responses using probabilistic neural networks. Chem. Res. Toxicol. 14(2), 182–191 (2001)
Huang, C.J.: A performance analysis of cancer classification using feature extraction and probabilistic neural networks. In: 7th Conference on Artificial Intelligence and Applications, Wufon, Taiwan, pp. 374–378 (2002)
Wang, Y., Adali, T., Kung, S., Szabo, Z.: Quantification and segmentation of brain tissues from mr images: A probabilistic neural network approach. IEEE Transactions on Image Processing 7(8), 1165–1181 (1998)
Ganchev, T., Tasoulis, D.K., Vrahatis, M.N., Fakotakis, N.: Generalized locally recurrent probabilistic neural networks with application to text-independent speaker verification. Neurocomputing 70(7-9), 1424–1438 (2007)
Romero, R., Touretzky, D., Thibadeau, R.: Optical chinese character recognition using probabilistic neural network. Pattern Recognition 8(30), 1279–1292 (1997)
Hand, J.D.: Kernel Discriminant Analysis. Research Studies Press (1982)
Georgiou, V.L., Pavlidis, N.G., Parsopoulos, K.E., Alevizos, P.D., Vrahatis, M.N.: New self–adaptive probabilistic neural networks in bioinformatic and medical tasks. International Journal on Artificial Intelligence Tools 15(3), 371–396 (2006)
Georgiou, V.L., Malefaki, S.N.: Incorporating Bayesian models for the estimation of the spread parameters of probabilistic neural networks with application in biomedical tasks. In: Int. Conf. on Statistical Methods for Biomedical and Technical Systems, Limassol, Cyprus, pp. 305–310 (2006)
Georgiou, V.L., Malefaki, S.N., Alevizos, P.D., Vrahatis, M.N.: Evolutionary Bayesian probabilistic neural networks. In: Int. Conf. on Numerical Analysis and Applied Mathematics (ICNAAM 2006), pp. 393–396. Wiley-VCH, Chichester (2006)
Raghu, P.P., Yegnanarayana, B.: Supervised texture classification using a probabilistic neural network and constraint satisfaction model. IEEE Transactions on Neural Networks 9(3), 516–522 (1998)
Parzen, E.: On the estimation of a probability density function and mode. Annals of Mathematical Statistics 3, 1065–1076 (1962)
Specht, D.F., Romsdahl, H.: Experience with adaptive probabilistic neural network and adaptive general regression neural network. In: Proc. IEEE Int. Conf. Neural Networks, vol. 2, pp. 1203–1208 (1994)
Kaufman, L., Rousseeuw, P.J.: Finding Groups in Data: An Introduction to Cluster Analysis. John Wiley and Sons, New York (1990)
Eberhart, R.C., Kennedy, J.: A new optimizer using particle swarm theory. In: Proceedings Sixth Symposium on Micro Machine and Human Science, Piscataway, NJ, pp. 39–43. IEEE Service Center, Los Alamitos (1995)
Kennedy, J., Eberhart, R.C.: Particle swarm optimization. In: Proc. IEEE Int. Conf. Neural Networks, vol. IV, pp. 1942–1948 (1995)
Clerc, M., Kennedy, J.: The particle swarm–explosion, stability, and convergence in a multidimensional complex space. IEEE Tr. Ev. Comp. 6(1), 58–73 (2002)
Geman, S., Geman, D.: Stochastic relaxation, gibbs distributions and the Bayesian restoration of images. IEEE Trans. Pattn. Anal. Mach. Intel. 6, 721–741 (1984)
Gilks, W.R., Richardson, S., Spiegelhalter, D.J.: Markov Chain Monte Carlo in Practice. Chapman and Hall, Boca Raton (1996)
Looney, C.G.: A fuzzy classifier network with ellipsoidal epanechnikov functions. Neurocomputing 48, 489–509 (2002)
Herrmann, E.: Asymptotic distribution of bandwidth selectors in kernel regression estimation. Statistical Papers 35, 17–26 (1994)
Prechelt, L.: Proben1: A set of neural network benchmark problems and benchmarking rules. Technical Report 21/94, Fak. Informatik, Univ. Karlsruhe (1994)
Asuncion, A., Newman, D.: UCI machine learning repository (2007)
Mangasarian, O.L., Wolberg, W.H.: Cancer diagnosis via linear programming. SIAM News 23(5), 1–18 (1990)
Quinlan, J.: Simplifying decision trees. International Journal of Man-Machine Studies 27(3), 221–234 (1987)
Smith, J.W., Everhart, J.E., Dickson, W.C., Knowler, W.C., Johannes, R.S.: Using the adap learning algorithm to forecast the onset of diabetes mellitus. In: Proc. Symp. Comp. Appl. & Med. Care, pp. 261–265 (1988)
Detrano, R., Janosi, A., Steinbrunn, W., Pfisterer, M., Schmid, J., Sandhu, S., Guppy, K., Lee, S., Froelicher, V.: International application of a new probability algorithm for the diagnosis of coronary artery disease. American Journal of Cardiology 64, 304–310 (1989)
Gorunescu, F., Gorunescu, M., Revett, K., Ene, M.: A hybrid incremental/monte carlo searching technique for the “smoothing” parameter of probabilistic neural networks. In: Int. Conf. Knowl. Eng., Princ. and Techn., KEPT 2007, Cluj-Napoca, Romania, pp. 107–113 (2007)
Georgiou, V.L., Alevizos, P.D., Vrahatis, M.N.: Novel approaches to probabilistic neural networks through bagging and evolutionary estimating of prior probabilities. Neural Processing Letters 27, 153–162 (2008)
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Georgiou, V.L., Malefaki, S., Parsopoulos, K.E., Alevizos, P.D., Vrahatis, M.N. (2009). Expeditive Extensions of Evolutionary Bayesian Probabilistic Neural Networks. In: Stützle, T. (eds) Learning and Intelligent Optimization. LION 2009. Lecture Notes in Computer Science, vol 5851. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-11169-3_3
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DOI: https://doi.org/10.1007/978-3-642-11169-3_3
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