Abstract
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.
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Available online at http://archive.ics.uci.edu/ml/.
References
Lopez J, Suykens Johan AK (2011) First and second order SMO algorithms for LS-SVM classifiers. Neural Process Lett 33(1):31–44
Vapnik VN (1995) The nature of statistical learning theory. Springer, Berlin
Chua KS (2003) Efficient computations for large least square support vector machine classifiers. Pattern Recognit Lett 24(1–3):75–80
Qinlan JR (1986) Introduction of decision trees. Mach Learn 1(1):86–106
Freund Y (1995) Boosting a weak learning algorithm by majority. Inf Comput 121(2):256–285
Hongjun L, Rudy S, Huan L (1996) Effect data mining using neural networks. IEEE Trans Knowl Data Eng 8(6):957–961
Misraa BB, Dehurib S, Dashc PK, Pandad G (2008) A reduced and comprehensible polynomial neural network for classification. Pattern Recognit Lett 29(12):1705–1712
Daqi G, Yan J (2005) Classification methodologies of multilayer perceptrons with sigmoid activation functions. Pattern Recognit 38(10):1469–1482
Hassan YF (2011) Rough sets for adapting wavelet neural networks as a new classifier system. Appl Intell 35(2):260–268
Castano A, Fernandez-Navarro F, Hervas-Martinez C et al (2011) Neuro-logistic models based on evolutionary generalized radial basis function for the microarray gene expression classification problem. Neural Process Lett 34(2):117–131
An S-Y, Kang J-G, Choi W-S, Oh S-Y (2011) A neural network based retrainable framework for robust object recognition with application to mobile robotics. Appl Intell 35(2):190–210
Avci E (2012) An expert target recognition system using a genetic wavelet neural network. Appl Intell 37(4):475–487
Yaakob SN, Jain L (2012) An insect classification analysis based on shape features using quality threshold ARTMAP and moment invariant. Appl Intell 37(1):12–30
Venkatesh YV, Kumar Raja S (2003) On the classification of multispectral satellite images using the multilayer perceptron. Pattern Recognit 36(9):2161–2175
Verma B, McLeod P, Klevansky A (2009) A novel soft cluster neural network for the classification of suspicious areas in digital mammograms. Pattern Recognit 42(9):1845–1852
Kang S, Park S (2009) A fusion neural network classifier for image classification. Pattern Recognit Lett 30(9):789–793
Wang L, Yang B, Chen Y et al (2012) Improvement of neural network classifier using floating centroids. Knowl Inf Syst 31(3):433–454
Czibula G, Gergely Czibula I, Dan Gaceanu R (2011) Intelligent data structures selection using neural networks. Knowl Inf Syst 34(1):171–192
Zhang L, Wang L, Wang X, Liu K, Abraham A (2012) Research of neural network classifier based on FCM and PSO for breast cancer classification. In: HAIS 2012, part I. Lecture notes in computer science, vol 7208, pp 647–654
Czarnowski I, Jedrzejowicz P (2012) Agent-based approach to RBF network training with floating centroids. In: The 4th international conference on computational collective intelligence, pp 453–462
Kwedlo W, Bandurski K (2006) A parallel differential evolution algorithm for neural network training. In: International symposium on parallel computing in electrical engineering, pp 319–324
Srinivasan N, Vaidehi V (2005) Cluster computing for neural network based amomaly detection. In: 13th IEEE international conference on networks jointly held with the 7th IEEE Malaysia international conference on communications, pp 130–134
Garcia-Nieto J, Alba E (2012) Parallel multi-swarm optimizer for gene selection in DNA microarrays. Appl Intell 37(2):255–266
Tobias P, Peter V, Wolfgang P et al (2009) GPU accelerated Monte Carlo simulation of the 2D and 3D Ising model. J Comput Phys 228(12):4468–4477
Guorui Y, Jie T, Shouping Z et al (2008) Fast cone-beam CT image reconstruction using GPU hardware. J X-Ray Sci Technol 16(4):225–234
Harvey MJ, De Fabritiis G (2009) An implementation of the smooth particle Mesh Ewald method on GPU hardware. J Chem Theory Comput 5(9):2371–2377
Guillem P, Garry C, Olcott PD et al (2009) Fast, accurate and shift-varying line projections for iterative reconstruction using the GPU. IEEE Trans Med Imaging 28(3):435–445
van der Laan Wladimir J, Jalba Andrei C, Roerdink Jos BTM (2011) Accelerating wavelet lifting on graphics hardware using CUDA. IEEE Trans Parallel Distrib Syst 22(1):132–146
Kennedy J, Eberhart RC (1995) A new optimizer using paritcle swarm theory. In: Proc. the sixth int. symposium on micromachine and human science, pp 39–43
Wang K, Zheng YJ (2012) A new particle swarm optimization algorithm for fuzzy optimization of armored vehicle scheme design. Appl Intell 37(4):520–526
Hartigan JA, Wong MA (1979) A k-means clustering algorithm. Appl Stat 28(1):100–108
Bridle JS (1990) Probabilistic interpretation of feedforward classification network outputs, with relationships to statistical pattern recognition. In: Neuralcomputing: algorithms, architectures and applications. Springer, Berlin, pp 227–236
Dietterich TG, Bakiri G (1995) Solving multiclass learning problems via error-correcting output codes. J Artif Intell Res 2(1):263–286
Che S, Boyer M, Meng J, Tarjan D, Sheaffer JW, Skadron K (2008) A performance study of general-purpose applications on graphics processors using CUDA. J Parallel Distrib Comput 68(10):1370–1380
Fang W, Lau KK, Lu M, Xiao X, Lam CK, Yang PY, He B, Luo Q, Sande PV, Yang K (2008) Parallel Data Mining on Graphics Processors. Technical Report HKUSTCS08
Wu J, Hong B (2011) An efficient k-means algorithm on CUDA. In: 2011 IEEE international symposium on parallel & distributed processing, workshops and phd forum, vol 2, pp 1740–1749
Acknowledgements
This work was supported by National Key Technology Research and Development Program of the Ministry of Science and Technology under Grant 2012BAF12B07-3. National Natural Science Foundation of China under Grant Nos. 61173078, 61203105, 61173079, 61070130, 60903176. Provincial Natural Science Foundation for Outstanding Young Scholars of Shandong under Grant No. JQ200820. Shandong Provincial Natural Science Foundation, China, under Grant Nos. ZR2010FM047, ZR2012FQ016, ZR2012FM010. Program for New Century Excellent Talents in University under Grant No. NCET-10-0863.
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Wang, L., Yang, B., Chen, Y. et al. Accelerating FCM neural network classifier using graphics processing units with CUDA. Appl Intell 40, 143–153 (2014). https://doi.org/10.1007/s10489-013-0450-8
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DOI: https://doi.org/10.1007/s10489-013-0450-8