Evolutionary Radial Basis Function Network for Classificatory Problems

Classification has been a major problem of study whose application includes speaker recognition, character recognition, etc. In this paper we first adapt the Radial Basis Function Network (RBFN) for classification problems and then use customized Evolutionary Algorithms to evolve the RBFN. The neurons of the RBFN correspond to some class out of the available output classes. Linear addition of only the same class neurons is taken and an additional layer is added that decides the final output on the basis of maximum activation of each class. Evolutionary algorithm has operators jump and add neuron that aid in optimization. Penalty has been used to restrict overgrowth of network. The algorithm was used to solve the problem of detection of PIMA Indian diabetes and gave a recognition rate of 82.37%, which was better than most of the commonly known algorithms in literature.

[1]  Loris Nanni,et al.  Particle swarm optimization for prototype reduction , 2009, Neurocomputing.

[2]  Bidyut Baran Chaudhuri,et al.  Efficient training and improved performance of multilayer perceptron in pattern classification , 2000, Neurocomputing.

[3]  Cheng-Jian Lin,et al.  An entropy-based quantum neuro-fuzzy inference system for classification applications , 2007, Neurocomputing.

[4]  Tanmoy Som,et al.  Handwritten Character Recognition by Using Neural-Network and Euclidean Distance Metric , 2012 .

[5]  Chien-Hsing Chou,et al.  A New Approach to Fuzzy Classifier Systems and Its Application in Self-generating Neuro-fuzzy Systems , 2022 .

[6]  Leszek Rutkowski,et al.  Flexible Neuro-fuzzy Systems: Structures, Learning and Performance Evaluation (Kluwer International Series in Engineering and Computer Science) , 2004 .

[7]  Neil Davey,et al.  Using a genetic algorithm to investigate efficient connectivity in associative memories , 2009, Neurocomputing.

[8]  Pedro Antonio Gutiérrez,et al.  Evolutionary product-unit neural networks classifiers , 2008, Neurocomputing.

[9]  Michael O'Neill,et al.  Grammatical Evolution: Evolving Programs for an Arbitrary Language , 1998, EuroGP.

[10]  Abdullah Al Mamun,et al.  Training neural networks for classification using growth probability-based evolution , 2008, Neurocomputing.

[11]  Horst Bunke,et al.  Off-line cursive handwriting recognition using hidden markov models , 1995, Pattern Recognit..

[12]  A. Graves,et al.  Unconstrained Online Handwriting Recognition with Recurrent Neural Networks , 2007 .

[13]  Leszek Rutkowski,et al.  Flexible neuro-fuzzy systems , 2003, IEEE Trans. Neural Networks.

[14]  H. JoséAntonioMartín,et al.  Hybridizing evolutionary computation and reinforcement learning for the design of almost universal controllers for autonomous robots , 2009, Neurocomputing.

[15]  Subhash C. Kak,et al.  On Generalization by Neural Networks , 1998, Inf. Sci..

[16]  F. J. Martı́nez-Estudilloa,et al.  Evolutionary product-unit neural networks classifiers , 2008 .

[17]  Anthony Brabazon,et al.  Recent Adventures in Grammatical Evolution , 2005 .

[18]  Conor Ryan,et al.  Grammatical Evolution , 2001, Genetic Programming Series.

[19]  Leszek Rutkowski,et al.  Flexible Neuro-Fuzzy Systems: Structures, Learning and Performance Evaluation—L. Rutkowski (Boston, MA: Kluwer Academic Publishers, 2004, ISBN: 1-402-08042-5) Reviewed by A. E. Gaweda , 2006, IEEE Transactions on Neural Networks.

[20]  Sanjika Hewavitharana,et al.  Off-Line Sinhala Handwriting Recognition Using Hidden Markov Models , 2002, ICVGIP.

[21]  Mohsen Soryani,et al.  Application of Genetic Algorithms to Feature Subset Selection in a Farsi OCR , 2008 .

[22]  Jürgen Schmidhuber,et al.  Unconstrained On-line Handwriting Recognition with Recurrent Neural Networks , 2007, NIPS.

[23]  Haitao Liu,et al.  Feature selection for handwritten Chinese character recognition based on genetic algorithms , 1998, SMC'98 Conference Proceedings. 1998 IEEE International Conference on Systems, Man, and Cybernetics (Cat. No.98CH36218).

[24]  Pritam Rajagopal The Basic Kak Neural Network with Complex Inputs , 2006, ArXiv.

[25]  C. W. Tao,et al.  A New Neuro-Fuzzy Classifier with Application to On-Line Face Detection and Recognition , 2000, J. VLSI Signal Process..

[26]  Euripidis Glavas,et al.  Neural network construction and training using grammatical evolution , 2008, Neurocomputing.

[27]  Conor Ryan,et al.  Grammatical evolution , 2007, GECCO '07.

[28]  Sorin Draghici,et al.  A Neural Network Based Artificial Vision System for Licence Plate Recognition , 1997, Int. J. Neural Syst..

[29]  Yi-Chung Hu,et al.  Nonadditive grey single-layer perceptron with Choquet integral for pattern classification problems using genetic algorithms , 2008, Neurocomputing.

[30]  Kazuyuki Murase,et al.  Single-layered complex-valued neural network for real-valued classification problems , 2009, Neurocomputing.

[31]  S Draghici,et al.  A NEURAL NETWORK BASED ARTIFICIAL VISION SYSTEM FOR LICENSE PLATE RECOGNITION , 1997 .

[32]  Cheng-Jian Lin,et al.  The design of neuro-fuzzy networks using particle swarm optimization and recursive singular value decomposition , 2007, Neurocomputing.

[33]  R. J. Kuo,et al.  Continuous genetic algorithm-based fuzzy neural network for learning fuzzy IF-THEN rules , 2008, Neurocomputing.

[34]  Armando Vieira,et al.  A training algorithm for classification of high-dimensional data , 2003, Neurocomputing.

[35]  Javier de Lope,et al.  Hybridizing evolutionary computation and reinforcement learning for the design of almost universal controllers for autonomous robots , 2009 .

[36]  Alfredo Álvarez,et al.  Sleep stage classification using fuzzy sets and machine learning techniques , 2004, Neurocomputing.

[37]  Inge Gavat,et al.  Neuro-fuzzy Models for Speech Pattern Recognition in Romanian Language , 1999 .

[38]  Meng Joo Er,et al.  A novel framework for automatic generation of fuzzy neural networks , 2008, Neurocomputing.

[39]  Hardeep Singh,et al.  A Comparative Analysis of Fuzzy, Neuro-Fuzzy and Fuzzy-GA Based Approaches for Software Reusability Evaluation , 2008 .

[40]  Hee Hyol Lee,et al.  A neuro-fuzzy classifier for land cover classification , 1999, FUZZ-IEEE'99. 1999 IEEE International Fuzzy Systems. Conference Proceedings (Cat. No.99CH36315).

[41]  Gernot A. Fink,et al.  Unsupervised Estimation of Writing Style Models for Improved Unconstrained Off-line Handwriting Recognition , 2006 .