Accelerating FCM neural network classifier using graphics processing units with CUDA

With the advancement in experimental devices and approaches, scientific data can be collected more easily. Some of them are huge in size. The floating centroids method (FCM) has been proven to be a high performance neural network classifier. However, the FCM is difficult to learn from a large data set, which restricts its practical application. In this study, a parallel floating centroids method (PFCM) is proposed to speed up the FCM based on the Compute Unified Device Architecture, especially for a large data set. This method performs all stages as a batch in one block. Blocks and threads are responsible for evaluating classifiers and performing subtasks, respectively. Experimental results indicate that the speed and accuracy are improved by employing this novel approach.

[1]  Gao Daqi,et al.  Classification methodologies of multilayer perceptrons with sigmoid activation functions , 2005, Pattern Recognit..

[2]  Gabriela Serban Czibula,et al.  Intelligent data structures selection using neural networks , 2011, Knowledge and Information Systems.

[3]  Krzysztof Bandurski,et al.  A Parallel Differential Evolution Algorithm A Parallel Differential Evolution Algorithm , 2006, PARELEC.

[4]  Jos B. T. M. Roerdink,et al.  Accelerating Wavelet Lifting on Graphics Hardware Using CUDA , 2011, IEEE Transactions on Parallel and Distributed Systems.

[5]  Kevin Skadron,et al.  A performance study of general-purpose applications on graphics processors using CUDA , 2008, J. Parallel Distributed Comput..

[6]  Bingsheng He,et al.  Parallel Data Mining on Graphics Processors , 2011 .

[7]  Yoav Freund,et al.  Boosting a weak learning algorithm by majority , 1990, COLT '90.

[8]  Hongjun Lu,et al.  Effective Data Mining Using Neural Networks , 1996, IEEE Trans. Knowl. Data Eng..

[9]  Yasser F. Hassan,et al.  Rough sets for adapting wavelet neural networks as a new classifier system , 2011, Applied Intelligence.

[10]  José García-Nieto,et al.  Parallel multi-swarm optimizer for gene selection in DNA microarrays , 2011, Applied Intelligence.

[11]  Jie Tian,et al.  Fast cone-beam CT image reconstruction using GPU hardware , 2008 .

[12]  Hongwei Sun,et al.  Improvement of neural network classifier using floating centroids , 2011, Knowledge and Information Systems.

[13]  J. Ross Quinlan,et al.  Induction of Decision Trees , 1986, Machine Learning.

[14]  Johan A. K. Suykens,et al.  First and Second Order SMO Algorithms for LS-SVM Classifiers , 2011, Neural Processing Letters.

[15]  Russell C. Eberhart,et al.  A new optimizer using particle swarm theory , 1995, MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science.

[16]  Engin Avci.  An expert target recognition system using a genetic wavelet neural network , 2012, Applied Intelligence.

[17]  Pedro Antonio Gutiérrez,et al.  Neuro-logistic Models Based on Evolutionary Generalized Radial Basis Function for the Microarray Gene Expression Classification Problem , 2011, Neural Processing Letters.

[18]  Brijesh Verma,et al.  A novel soft cluster neural network for the classification of suspicious areas in digital mammograms , 2009, Pattern Recognit..

[19]  Sanggil Kang,et al.  A fusion neural network classifier for image classification , 2009, Pattern Recognit. Lett..

[20]  Yujun Zheng,et al.  A new particle swarm optimization algorithm for fuzzy optimization of armored vehicle scheme design , 2012, Applied Intelligence.

[21]  Se-Young Oh,et al.  A neural network based retrainable framework for robust object recognition with application to mobile robotics , 2011, Applied Intelligence.

[22]  Y. V. Venkatesh,et al.  On the classification of multispectral satellite images using the multilayer perceptron , 2003, Pattern Recognit..

[23]  N. Srinivasan,et al.  Cluster computing for neural network based anomaly detection , 2005, 2005 13th IEEE International Conference on Networks Jointly held with the 2005 IEEE 7th Malaysia International Conf on Communic.

[24]  Craig S. Levin,et al.  Fast, Accurate and Shift-Varying Line Projections for Iterative Reconstruction Using the GPU , 2009, IEEE Transactions on Medical Imaging.

[25]  Lakhmi C. Jain,et al.  An insect classification analysis based on shape features using quality threshold ARTMAP and moment invariant , 2011, Applied Intelligence.

[26]  Lior Rokach,et al.  An Introduction to Decision Trees , 2007 .

[27]  Piotr Jedrzejowicz,et al.  Agent-Based Approach to RBF Network Training with Floating Centroids , 2012, ICCCI.

[28]  Wolfgang Paul,et al.  GPU accelerated Monte Carlo simulation of the 2D and 3D Ising model , 2009, J. Comput. Phys..

[29]  Kok Seng Chua,et al.  Efficient computations for large least square support vector machine classifiers , 2003, Pattern Recognit. Lett..

[30]  J. A. Hartigan,et al.  A k-means clustering algorithm , 1979 .

[31]  Lei Zhang,et al.  Research of Neural Network Classifier Based on FCM and PSO for Breast Cancer Classification , 2012, HAIS.

[32]  Ganapati Panda,et al.  A reduced and comprehensible polynomial neural network for classification , 2008, Pattern Recognit. Lett..

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

[34]  Thomas G. Dietterich,et al.  Solving Multiclass Learning Problems via Error-Correcting Output Codes , 1994, J. Artif. Intell. Res..

[35]  John Scott Bridle,et al.  Probabilistic Interpretation of Feedforward Classification Network Outputs, with Relationships to Statistical Pattern Recognition , 1989, NATO Neurocomputing.

[36]  K. Bandurski,et al.  A parallel differential evolution algorithm for neural network training , 2006, International Symposium on Parallel Computing in Electrical Engineering (PARELEC'06).

[37]  Bo Hong,et al.  An Efficient k-Means Algorithm on CUDA , 2011, 2011 IEEE International Symposium on Parallel and Distributed Processing Workshops and Phd Forum.

[38]  M J Harvey,et al.  An Implementation of the Smooth Particle Mesh Ewald Method on GPU Hardware. , 2009, Journal of chemical theory and computation.