Weighted probabilistic neural network

Abstract In this work, the modification of the probabilistic neural network (PNN) is proposed. The traditional network is adjusted by introducing the weight coefficients between pattern and summation layer. The weights are derived using the sensitivity analysis (SA) procedure. The performance of the weighted PNN (WPNN) is examined in data classification problems on benchmark data sets. The obtained WPNN’s efficiency results are compared with these achieved by a modified PNN model put forward in literature, the original PNN and selected state-of-the-art classification algorithms: support vector machine, multilayer perceptron, radial basis function neural network, k-nearest neighbor method and gene expression programming algorithm. All classifiers are collated by computing the prediction accuracy obtained with the use of a k-fold cross validation procedure. It is shown that in seven out of ten classification cases, WPNN outperforms both the weighted PNN classifier introduced in literature and the original model. Furthermore, according to the ranking statistics, the proposed WPNN takes the first place among all tested algorithms.

[1]  Xiao-Jun Zeng,et al.  Density-Driven Generalized Regression Neural Networks (DD-GRNN) for Function Approximation , 2007, IEEE Transactions on Neural Networks.

[2]  Mo M. Jamshidi,et al.  A novel weighted probabilistic neural network for MR image segmentation , 2005, 2005 IEEE International Conference on Systems, Man and Cybernetics.

[3]  B. Silverman.  Density estimation for statistics and data analysis , 1986 .

[4]  Dominik Slezak,et al.  Feedforward neural networks for compound signals , 2011, Theor. Comput. Sci..

[5]  D. Broomhead,et al.  Radial Basis Functions, Multi-Variable Functional Interpolation and Adaptive Networks , 1988 .

[6]  Maciej Kusy,et al.  Probabilistic neural network training procedure based on Q(0)-learning algorithm in medical data classification , 2014, Applied Intelligence.

[7]  Umamaheswari krishnasamy,et al.  Hybrid weighted probabilistic neural network and biogeography based optimization for dynamic economic dispatch of integrated multiple-fuel and wind power plants , 2016 .

[8]  Longbing Cao,et al.  Novel Intrusion Detection using Probabilistic Neural Network and Adaptive Boosting , 2009, ArXiv.

[9]  Kjell Elenius,et al.  Multi-layer perceptrons and probabilistic neural networks for phoneme recognition , 1993, EUROSPEECH.

[10]  Donald F. Specht,et al.  A general regression neural network , 1991, IEEE Trans. Neural Networks.

[11]  Hua Zhang,et al.  A new watermarking approach based on probabilistic neural network in wavelet domain , 2008, Soft Comput..

[12]  Sheng Chen,et al.  Experiments with repeating weighted boosting search for optimization signal processing applications , 2005, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[13]  Fan Zhou,et al.  Field-programmable gate array implementation of a probabilistic neural network for motor cortical decoding in rats , 2010, Journal of Neuroscience Methods.

[14]  S. Ramakrishnan,et al.  Image texture classification using wavelet based curve fitting and probabilistic neural network , 2007 .

[15]  Piotr A. Kowalski,et al.  Data Sample Reduction for Classification of Interval Information Using Neural Network Sensitivity Analysis , 2010, AIMSA.

[16]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[17]  Hojjat Adeli,et al.  A probabilistic neural network for earthquake magnitude prediction , 2009, Neural Networks.

[18]  Leszek Rutkowski,et al.  New Soft Computing Techniques for System Modeling, Pattern Classification and Image Processing , 2004 .

[19]  Dominik Ślęzak,et al.  Neural Network Architecture for Synthesis of the Probabilistic Rule Based Classifiers , 2003, RSKD.

[20]  Piotr A. Kowalski,et al.  Modification of the probabilistic neural network with the use of sensitivity analysis procedure , 2016, 2016 Federated Conference on Computer Science and Information Systems (FedCSIS).

[21]  S. Ramakrishnan,et al.  Comparative study between traditional and modified probabilistic neural networks , 2009, Telecommun. Syst..

[22]  Jacek M. Zurada,et al.  Sensitivity analysis for minimization of input data dimension for feedforward neural network , 1994, Proceedings of IEEE International Symposium on Circuits and Systems - ISCAS '94.

[23]  Jacek M. Zurada,et al.  Perturbation method for deleting redundant inputs of perceptron networks , 1997, Neurocomputing.

[24]  Piotr A. Kowalski,et al.  Interval probabilistic neural network , 2015, Neural Computing and Applications.

[25]  Donald F. Specht,et al.  Probabilistic neural networks , 1990, Neural Networks.

[26]  Martin Fodslette Møller,et al.  A scaled conjugate gradient algorithm for fast supervised learning , 1993, Neural Networks.

[27]  S. Gopal,et al.  Orthogonal least square center selection technique - A robust scheme for multiple source Partial Discharge pattern recognition using Radial Basis Probabilistic Neural Network , 2011, Expert Syst. Appl..

[28]  Leszek Rutkowski,et al.  Adaptive probabilistic neural networks for pattern classification in time-varying environment , 2004, IEEE Transactions on Neural Networks.

[29]  George C. Anastassopoulos,et al.  Genetic algorithm pruning of probabilistic neural networks in medical disease estimation , 2011, Neural Networks.

[30]  Ron Kohavi,et al.  A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection , 1995, IJCAI.

[31]  Evor L. Hines,et al.  Comparison of neural network predictors in the classification of tracheal-bronchial breath sounds by respiratory auscultation , 2004, Artif. Intell. Medicine.

[32]  Cândida Ferreira,et al.  Gene Expression Programming: A New Adaptive Algorithm for Solving Problems , 2001, Complex Syst..

[33]  H. Jeremy Bockholt,et al.  A hybrid tissue segmentation approach for brain MR images , 2006, Medical and Biological Engineering and Computing.

[34]  Donald F. Specht,et al.  Probabilistic neural networks and the polynomial Adaline as complementary techniques for classification , 1990, IEEE Trans. Neural Networks.

[35]  Piotr Duda,et al.  Knowledge discovery in data streams with the orthogonal series-based generalized regression neural networks , 2017, Inf. Sci..

[36]  Pohsiang Tsai,et al.  BSPNN: boosted subspace probabilistic neural network for email security , 2011, Artificial Intelligence Review.

[37]  Ronald Marsh,et al.  Conjugate gradient and approximate Newton methods for an optimal probabilistic neural network for food color classification , 1998 .

[38]  Maciej Kusy,et al.  Assessment of prediction ability for reduced probabilistic neural network in data classification problems , 2017, Soft Comput..

[39]  David J. Montana,et al.  A Weighted Probabilistic Neural Network , 1991, NIPS.

[40]  Peter E. Hart,et al.  Nearest neighbor pattern classification , 1967, IEEE Trans. Inf. Theory.

[41]  Maciej Kusy,et al.  Application of Reinforcement Learning Algorithms for the Adaptive Computation of the Smoothing Parameter for Probabilistic Neural Network , 2015, IEEE Transactions on Neural Networks and Learning Systems.

[42]  Piotr A. Kowalski,et al.  A Complete Algorithm for the Reduction of Pattern Data in the Classification of Interval Information , 2016 .