Competitive probabilistic neural network

Probabilistic neural networks (PNNs) are artificial neural network algorithms widely used in pattern recognition and classification problems. In the traditional PNN algorithm, the probability density function (PDF) is approximated using the entire training dataset for each class. In some complex datasets, classmate clusters may be located far from each other and these distances between clusters may cause a reduction in the correct class’s posterior probability and lead to misclassification. This paper presents a novel PNN algorithm, the competitive probabilistic neural network (CPNN). In the CPNN, a competitive layer ranks kernels for each class and an optimum fraction of kernels are selected to estimate the class-conditional probability. Using a stratified, repeated, random subsampling cross-validation procedure and 9 benchmark classification datasets, CPNN is compared to both traditional PNN and the state of the art (e.g. enhanced probabilistic neural network, EPNN). These datasets are examined with and without noise and the algorithm is evaluated with several ratios of training to testing data. In all datasets (225 simulation categories), performance percentages of both CPNN and EPNN are greater than or equivalent to that of the traditional PNN; in 73% of simulation categories, the CPNN analyses show modest improvement in performance over the state of the art.

[1]  Bayya Yegnanarayana,et al.  Supervised texture classification using a probabilistic neural network and constraint satisfaction model , 1998, IEEE Trans. Neural Networks.

[2]  Nicos G. Pavlidis,et al.  New Self-adaptive Probabilistic Neural Networks in Bioinformatic and Medical Tasks , 2006, Int. J. Artif. Intell. Tools.

[3]  Michael N. Vrahatis,et al.  Fuzzy Evolutionary Probabilistic Neural Networks , 2008, ANNPR.

[4]  H Prasanna Kumar,et al.  Classification of ovary abnormality using the probabilistic neural network (PNN). , 2014, Technology and health care : official journal of the European Society for Engineering and Medicine.

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

[6]  Saeed Lotfifard,et al.  Modeling and Health Monitoring of DC Side of Photovoltaic Array , 2015, IEEE Transactions on Sustainable Energy.

[7]  Mehdi Khashei,et al.  Hybridization of the probabilistic neural networks with feed-forward neural networks for forecasting , 2012, Eng. Appl. Artif. Intell..

[8]  Mohammad Bagher Menhaj,et al.  A soft probabilistic neural network for implementation of Bayesian classifiers , 2001, IJCNN'01. International Joint Conference on Neural Networks. Proceedings (Cat. No.01CH37222).

[9]  D. F. Specht,et al.  Probabilistic neural networks for classification, mapping, or associative memory , 1988, IEEE 1988 International Conference on Neural Networks.

[10]  F. Gubina,et al.  Allocation of the load profiles to consumers using probabilistic neural networks , 2005, IEEE Transactions on Power Systems.

[11]  Jin-Juan Quan,et al.  Multiscale probabilistic neural network method for SAR image segmentation , 2008, Appl. Math. Comput..

[12]  Hojjat Adeli,et al.  Neuro‐genetic algorithm for non‐linear active control of structures , 2008 .

[13]  Brett A. Story.  Structural Impairment Detection Using Arrays of Competitive Artificial Neural Networks , 2012 .

[14]  Hong Wang,et al.  EEG-based expert system using complexity measures and probability density function control in alpha sub-band , 2013, Integr. Comput. Aided Eng..

[15]  Hans G. C. Tråvén,et al.  A neural network approach to statistical pattern classification by 'semiparametric' estimation of probability density functions , 1991, IEEE Trans. Neural Networks.

[16]  P. K. Patra,et al.  Probabilistic neural network for pattern classification , 2002, Proceedings of the 2002 International Joint Conference on Neural Networks. IJCNN'02 (Cat. No.02CH37290).

[17]  Yuping Wang,et al.  Multiobjective bilevel optimization for production-distribution planning problems using hybrid genetic algorithm , 2014, Integr. Comput. Aided Eng..

[18]  Elif Derya Übeyli.  Probabilistic neural networks employing Lyapunov exponents for analysis of Doppler ultrasound signals , 2008, Comput. Biol. Medicine.

[19]  Fazhi He,et al.  A hybrid optimization approach for sustainable process planning and scheduling , 2018, Integr. Comput. Aided Eng..

[20]  Jase D. Sitton,et al.  Estimating Soil Classification Via Quantitative and Qualitative Field Testing for Use in Constructing Compressed Earth Blocks , 2016 .

[21]  Hojjat Adeli,et al.  A parallel genetic/neural network learning algorithm for MIMD shared memory machines , 1994, IEEE Trans. Neural Networks.

[22]  Salwani Abdullah,et al.  Hybridizing firefly algorithms with a probabilistic neural network for solving classification problems , 2015, Appl. Soft Comput..

[23]  R.P. Maheshwari,et al.  Power Transformer Differential Protection Based On Optimal Probabilistic Neural Network , 2010, IEEE Transactions on Power Delivery.

[24]  Abdelkader Benyettou,et al.  Over-fitting avoidance in probabilistic neural networks , 2015, 2015 World Congress on Information Technology and Computer Applications (WCITCA).

[25]  Hojjat Adeli,et al.  FUZZY-WAVELET RBFNN MODEL FOR FREEWAY INCIDENT DETECTION , 2000 .

[26]  E. B. Manoukian.  Modern concepts and theorems of mathematical statistics , 1985 .

[27]  Tetsuya Hoya,et al.  On the capability of accommodating new classes within probabilistic neural networks , 2003, IEEE Trans. Neural Networks.

[28]  Donald F. Specht,et al.  Generation of Polynomial Discriminant Functions for Pattern Recognition , 1967, IEEE Trans. Electron. Comput..

[29]  Ahmad Taher Azar,et al.  Probabilistic neural network for breast cancer classification , 2012, Neural Computing and Applications.

[30]  Jing Wang,et al.  A wavelet-based particle swarm optimization algorithm for digital image watermarking , 2012, Integr. Comput. Aided Eng..

[31]  Yasha Zeinali,et al.  Structural Impairment Detection Using Deep Counter Propagation Neural Networks , 2016 .

[32]  Anis Yazidi,et al.  A pattern recognition approach for peak prediction of electrical consumption , 2016, Integr. Comput. Aided Eng..

[33]  Yinan Yu,et al.  A robust subspace classification scheme based on empirical intersection removal and sparse approximation , 2015, Integr. Comput. Aided Eng..

[34]  Sharon L. Wood,et al.  Probabilistic method for estimating remaining fatigue life in steel bridges using measured strain data , 2013 .

[35]  Hojjat Adeli,et al.  Principal Component Analysis-Enhanced Cosine Radial Basis Function Neural Network for Robust Epilepsy and Seizure Detection , 2008, IEEE Transactions on Biomedical Engineering.

[36]  A. A. Egorov,et al.  Use of probabilistic neural networks to solve classification problems in surgery , 2011, Optical Memory and Neural Networks.

[37]  Elif Derya íbeyli.  Probabilistic neural networks employing Lyapunov exponents for analysis of Doppler ultrasound signals , 2008 .

[38]  Anurag Pande,et al.  A Computing Approach Using Probabilistic Neural Networks for Instantaneous Appraisal of Rear‐End Crash Risk , 2008, Comput. Aided Civ. Infrastructure Eng..

[39]  Octavia I. Camps,et al.  Weighted Parzen Windows for Pattern Classification , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[40]  Anis Yazidi,et al.  A pattern recognition approach for peak prediction of electrical consumption , 2014, Integr. Comput. Aided Eng..

[41]  Simaan M. AbouRizk,et al.  ESTIMATING LABOR PRODUCTIVITY USING PROBABILITY INFERENCE NEURAL NETWORK , 2000 .

[42]  A. Savchenko,et al.  Pattern recognition and increasing of the computational efficiency of a parallel realization of the probabilistic neural network with homogeneity testing , 2013, Optical Memory and Neural Networks.

[43]  Wee Ser,et al.  Probabilistic neural-network structure determination for pattern classification , 2000, IEEE Trans. Neural Networks Learn. Syst..

[44]  Kuan-Cheng Lin,et al.  Supervised Learning Probabilistic Neural Networks , 2011, Neural Processing Letters.

[45]  Robert P. W. Duin,et al.  On the Choice of Smoothing Parameters for Parzen Estimators of Probability Density Functions , 1976, IEEE Transactions on Computers.

[46]  Hojjat Adeli,et al.  Computer-Aided Diagnosis of Parkinson’s Disease Using Enhanced Probabilistic Neural Network , 2015, Journal of Medical Systems.

[47]  T. Cacoullos.  Estimation of a multivariate density , 1966 .

[48]  Hyo Seon Park,et al.  COUNTERPROPAGATION NEURAL NETWORKS IN STRUCTURAL ENGINEERING , 1995 .

[49]  Buse Melis Ozyildirim,et al.  One pass learning for generalized classifier neural network , 2016, Neural Networks.

[50]  Hojjat Adeli,et al.  RADIAL BASIS FUNCTION NEURAL NETWORK FOR WORK ZONE CAPACITY AND QUEUE ESTIMATION , 2003 .

[51]  E. Parzen.  On Estimation of a Probability Density Function and Mode , 1962 .

[52]  Chenn-Jung Huang,et al.  Application of Probabilistic Neural Networks to the Class Prediction of Leukemia and Embryonal Tumor of Central Nervous System , 2004, Neural Processing Letters.

[53]  Pietro Burrascano,et al.  Learning vector quantization for the probabilistic neural network , 1991, IEEE Trans. Neural Networks.

[54]  Hojjat Adeli,et al.  Enhanced probabilistic neural network with local decision circles: A robust classifier , 2010, Integr. Comput. Aided Eng..

[55]  Andrey V. Savchenko,et al.  Probabilistic neural network with homogeneity testing in recognition of discrete patterns set , 2013, Neural Networks.

[56]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[57]  Geoffrey E. Hinton,et al.  Learning internal representations by error propagation , 1986 .

[58]  Brigitte Chebel-Morello,et al.  Linear feature selection and classification using PNN and SFAM neural networks for a nearly online diagnosis of bearing naturally progressing degradations , 2015, Eng. Appl. Artif. Intell..

[59]  Brett A. Story,et al.  Methodology for Designing Diagnostic Data Streams for Use in a Structural Impairment Detection System , 2014 .

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

[61]  Anne Ng,et al.  Predicting Structural Deterioration Condition of Individual Storm-Water Pipes Using Probabilistic Neural Networks and Multiple Logistic Regression Models , 2009 .

[62]  P ? ? ? ? ? ? ? % ? ? ? ? , 1991 .

[63]  D. Lindley.  Bayes theory , 1984 .

[64]  James A. Reggia,et al.  Causally-guided evolutionary optimization and its application to antenna array design , 2012, Integr. Comput. Aided Eng..

[65]  Mehdi Khashei,et al.  Hybridization of autoregressive integrated moving average (ARIMA) with probabilistic neural networks (PNNs) , 2012, Comput. Ind. Eng..

[66]  Chung Bang Yun,et al.  Damage localization for bridges using probabilistic neural networks , 2007 .

[67]  Joydeep Ghosh,et al.  Using metaheuristics to optimize the combination of classifier and cluster ensembles , 2015, Integr. Comput. Aided Eng..

[68]  Hojjat Adeli,et al.  Spiking Neural Networks , 2009, Int. J. Neural Syst..

[69]  Peng Li,et al.  Structural damage localization using probabilistic neural networks , 2011, Math. Comput. Model..

[70]  Sumei Li,et al.  An improved method of speech recognition based on probabilistic neural network ensembles , 2015, 2015 11th International Conference on Natural Computation (ICNC).

[71]  Paul J. J. F. Torfs,et al.  Local probabilistic neural networks in hydrology , 2001 .

[72]  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.

[73]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[74]  Michael N. Vrahatis,et al.  Novel Approaches to Probabilistic Neural Networks Through Bagging and Evolutionary Estimating of Prior Probabilities , 2008, Neural Processing Letters.

[75]  Anastasios Rigos,et al.  A Chebyshev polynomial radial basis function neural network for automated shoreline extraction from coastal imagery , 2016, Integr. Comput. Aided Eng..

[76]  Jawad Iounousse,et al.  Using an unsupervised approach of Probabilistic Neural Network (PNN) for land use classification from multitemporal satellite images , 2015, Appl. Soft Comput..

[77]  Bijaya Ketan Panigrahi,et al.  High impedance fault detection in power distribution networks using time-frequency transform and probabilistic neural network , 2008 .

[78]  Brett A. Story,et al.  A Structural Impairment Detection System Using Competitive Arrays of Artificial Neural Networks , 2014, Comput. Aided Civ. Infrastructure Eng..

[79]  Y. L. Tong.  The multivariate normal distribution , 1989 .

[80]  Jun-Hai Zhai,et al.  Ensemble of multiresolution probabilistic neural network classifiers with fuzzy integral for face recognition , 2016, J. Intell. Fuzzy Syst..

[81]  Srinivasan Ramakrishnan,et al.  Image texture classification using wavelet packet transform and probabilistic neural network , 2007, Intell. Data Anal..