One pass learning for generalized classifier neural network

Generalized classifier neural network introduced as a kind of radial basis function neural network, uses gradient descent based optimized smoothing parameter value to provide efficient classification. However, optimization consumes quite a long time and may cause a drawback. In this work, one pass learning for generalized classifier neural network is proposed to overcome this disadvantage. Proposed method utilizes standard deviation of each class to calculate corresponding smoothing parameter. Since different datasets may have different standard deviations and data distributions, proposed method tries to handle these differences by defining two functions for smoothing parameter calculation. Thresholding is applied to determine which function will be used. One of these functions is defined for datasets having different range of values. It provides balanced smoothing parameters for these datasets through logarithmic function and changing the operation range to lower boundary. On the other hand, the other function calculates smoothing parameter value for classes having standard deviation smaller than the threshold value. Proposed method is tested on 14 datasets and performance of one pass learning generalized classifier neural network is compared with that of probabilistic neural network, radial basis function neural network, extreme learning machines, and standard and logarithmic learning generalized classifier neural network in MATLAB environment. One pass learning generalized classifier neural network provides more than a thousand times faster classification than standard and logarithmic generalized classifier neural network. Due to its classification accuracy and speed, one pass generalized classifier neural network can be considered as an efficient alternative to probabilistic neural network. Test results show that proposed method overcomes computational drawback of generalized classifier neural network and may increase the classification performance.

[1]  W. Land,et al.  A new training algorithm for the general regression neural network , 1997, 1997 IEEE International Conference on Systems, Man, and Cybernetics. Computational Cybernetics and Simulation.

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

[3]  Aaron C. Zecchin,et al.  Selection of smoothing parameter estimators for general regression neural networks - Applications to hydrological and water resources modelling , 2014, Environ. Model. Softw..

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

[5]  I-Cheng Yeh,et al.  Knowledge discovery on RFM model using Bernoulli sequence , 2009, Expert Syst. Appl..

[6]  O. Mangasarian,et al.  Multisurface method of pattern separation for medical diagnosis applied to breast cytology. , 1990, Proceedings of the National Academy of Sciences of the United States of America.

[7]  F. Pukelsheim The Three Sigma Rule , 1994 .

[8]  Buse Melis Ozyildirim,et al.  Generalized classifier neural network , 2013, Neural Networks.

[9]  O. Mangasarian,et al.  Robust linear programming discrimination of two linearly inseparable sets , 1992 .

[10]  Xiaojun Wu,et al.  Blind Image Quality Assessment Using a General Regression Neural Network , 2011, IEEE Transactions on Neural Networks.

[11]  Buse Melis Ozyildirim,et al.  Logarithmic learning for generalized classifier neural network , 2014, Neural Networks.

[12]  Ju H. Park,et al.  Effects of leakage time-varying delays in Markovian jump neural networks with impulse control , 2013, Neurocomputing.

[13]  Ju H. Park,et al.  Exponential stability of Markovian jumping stochastic Cohen-Grossberg neural networks with mode-dependent probabilistic time-varying delays and impulses , 2014, Neurocomputing.

[14]  Chee Peng Lim,et al.  A hybrid neural network model for noisy data regression , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[15]  Hongyi Li,et al.  An Incremental Learning Method Based on Probabilistic Neural Networks and Adjustable Fuzzy Clustering for Human Activity Recognition by Using Wearable Sensors , 2012, IEEE Transactions on Information Technology in Biomedicine.

[16]  R. Rakkiyappan,et al.  Stability of stochastic neural networks of neutral type with Markovian jumping parameters: A delay-fractioning approach , 2014, J. Frankl. Inst..

[17]  K. Nose-Filho,et al.  Short-Term Multinodal Load Forecasting Using a Modified General Regression Neural Network , 2011, IEEE Transactions on Power Delivery.

[18]  O. Mangasarian,et al.  Pattern Recognition Via Linear Programming: Theory and Application to Medical Diagnosis , 1989 .