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Improving Neural Network Classifier Using Gradient-Based Floating Centroid Method

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Part of the Communications in Computer and Information Science book series (CCIS,volume 1143)


Floating centroid method (FCM) offers an efficient way to solve a fixed-centroid problem for the neural network classifiers. However, evolutionary computation as its optimization method restrains the FCM to achieve satisfactory performance for different neural network structures, because of the high computational complexity and inefficiency. Traditional gradient-based methods have been extensively adopted to optimize the neural network classifiers. In this study, a gradient-based floating centroid (GDFC) method is introduced to address the fixed centroid problem for the neural network classifiers optimized by gradient-based methods. Furthermore, a new loss function for optimizing GDFC is introduced. The experimental results display that GDFC obtains promising classification performance than the comparison methods on the benchmark datasets.


  • Neural network classifier
  • Classification
  • Loss function
  • Floating Centroid Method

M. Islam and S. Liu—Both authors contribute equally to this article.

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  • DOI: 10.1007/978-3-030-36802-9_45
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  1. Bridle, J.S.: Probabilistic interpretation of feedforward classification network outputs, with relationships to statistical pattern recognition. In: Soulié, F.F., Hérault, J. (eds.) Neurocomputing. NATO ASI Series, vol. 68, pp. 227–236. Springer, Heidelberg (1990).

    CrossRef  Google Scholar 

  2. Dietterich, T.G., Bakiri, G.: Solving multiclass learning problems via error-correcting output codes. J. Artif. Int. Res. 2(1), 263–286 (1995)

    MATH  Google Scholar 

  3. Jiang, G., He, H., Yan, J., Xie, P.: Multiscale convolutional neural networks for fault diagnosis of wind turbine gearbox. IEEE Trans. Ind. Electron. PP, 1 (2018)

    Google Scholar 

  4. Kamilaris, A., Prenafeta-Bold, F.X.: A review of the use of convolutional neural networks in agriculture. J. Agric. Sci. 156(3), 312–322 (2018).

    CrossRef  Google Scholar 

  5. Nazari, M., Oroojlooy, A., Snyder, L., Takac, M.: Reinforcement learning for solving the vehicle routing problem. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31, pp. 9839–9849. Curran Associates, Inc. (2018)

    Google Scholar 

  6. Wang, L., Yang, B., Chen, Y., Zhang, X., Orchard, J.: Improving neural-network classifiers using nearest neighbor partitioning. IEEE Trans. Neural Netw. Learn. Syst. 28(10), 2255–2267 (2017)

    CrossRef  MathSciNet  Google Scholar 

  7. Wang, L., et al.: Improvement of neural network classifier using floating centroids. Knowl. Inf. Syst. 31(3), 433–454 (2012)

    CrossRef  Google Scholar 

  8. Wang, L., Yang, B., Chen, Z., Abraham, A., Peng, L.: A novel improvement of neural network classification using further division of partition space. In: Mira, J., Álvarez, J.R. (eds.) IWINAC 2007. LNCS, vol. 4527, pp. 214–223. Springer, Heidelberg (2007).

    CrossRef  Google Scholar 

  9. Wibowo, A., Wiryawan, P.W., Nuqoyati, N.I.: Optimization of neural network for cancer microRNA biomarkers classification. J. Phys: Conf. Ser. 1217, 012124 (2019)

    Google Scholar 

  10. Wong, Y.J., Arumugasamy, S.K., Jewaratnam, J.: Performance comparison of feedforward neural network training algorithms in modeling for synthesis of polycaprolactone via biopolymerization. Clean Technol. Environ. Policy 20(9), 1971–1986 (2018)

    CrossRef  Google Scholar 

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This work was supported by National Natural Science Foundation of China under Grant No. 61872419, No. 61573166, No. 61572230, No. 61873324, No. 81671785, No. 61672262. Shandong Provincial Natural Science Foundation No. ZR2019MF040, No. ZR2018LF005. Shandong Provincial Key R&D Program under Grant No. 2019GGX101041, No. 2018GGX101048, No. 2016ZDJS01A12, No. 2016GGX101001, No. 2017CXZC1206. Taishan Scholar Project of Shandong Province, China, under Grant No. tsqn201812077.

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Islam, M., Liu, S., Zhang, X., Wang, L. (2019). Improving Neural Network Classifier Using Gradient-Based Floating Centroid Method. In: Gedeon, T., Wong, K., Lee, M. (eds) Neural Information Processing. ICONIP 2019. Communications in Computer and Information Science, vol 1143. Springer, Cham.

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