Advertisement

Fuzzy Classifier with Convolution for Classification of Handwritten Digits

  • Rui YinEmail author
  • Wei Lu
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1000)

Abstract

Traditional fuzzy classifier is an important part of artificial intelligence. It achieves classification based on membership function and fuzzy rules which can deal with the uncertainty of data and has semantics. However, the definition of fuzzy rules requires prior knowledge. And fuzzy rules is too sample to achieve high accuracy of classification for classification of handwritten digits. The classifier proposed in this paper combines convolution with fuzzy classifier to classify handwritten digits. The classifier can be divided into two parts: convolution feature extraction part and Gauss membership calculation part. Using back propagation algorithm, the classifier parameters are trained by a large number of labeled data. It can independently extract useful features of handwritten digits to build handwriting feature prototypes, and establish membership functions according to feature prototypes. Experiments on MNIST datasets show that, compared with traditional fuzzy classifiers, the proposed fuzzy classifier can greatly improve the accuracy with less raised time complexity. For MNIST datasets, the proposed fuzzy classifier with convolution can reach higher classification accuracy.

References

  1. 1.
    Alex, K., Ilya, S., Geoffrey, E.H.: Imagenet classification with deep convolutional neural networks. Adv. Neural Inf. Process. Syst. 25, 1097–1105 (2012)Google Scholar
  2. 2.
    Arthur, D., Vassilvitskii, S.: k-means++: The advantages of careful seeding. In: Proceedings of the Eighteenth Annual ACM-SIAM Symposium on Discrete Algorithms, pp. 1027–1035. Society for Industrial and Applied Mathematics (2007)Google Scholar
  3. 3.
    Chuang, K.S., Tzeng, H.L., Chen, S., Wu, J., Chen, T.J.: Fuzzy c-means clustering with spatial information for image segmentation. Comput. Med. Imaging Graph. 30(1), 9–15 (2006)CrossRefGoogle Scholar
  4. 4.
    Duan, X., Wang, Y., Pedrycz, W., Liu, X., Wang, C., Li, Z.: AFSNN: a classification algorithm using axiomatic fuzzy sets and neural networks. IEEE Trans. Fuzzy Syst. 26(5), 3151–3163 (2018)CrossRefGoogle Scholar
  5. 5.
    Deng, Y., Ren, Z., Kong, Y., Bao, F., Dai, Q.: A hierarchical fused fuzzy deep neural network for data classification. IEEE Trans. Fuzzy Syst. 25(4), 1006–1012 (2017)CrossRefGoogle Scholar
  6. 6.
    Fan, H.W., Zhang, G.Y., Ding, A.L., Xie, C.R., Xu, T.: Improved BP algorithm and its application in detection of pavement crack. J. Chang’an Univ. 30(1), 438–457 (2010)Google Scholar
  7. 7.
    Hameed, I.A.: Using gaussian membership functions for improving the reliability and robustness of students’ evaluation systems. Expert Syst. Appl. 38(6), 7135–7142 (2011)CrossRefGoogle Scholar
  8. 8.
    Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
  9. 9.
    Kulkarni, A.D., Lulla, K.: Fuzzy neural network models for supervised classification: multispectral image analysis. Geocarto Int. 14(4), 42–51 (1999)CrossRefGoogle Scholar
  10. 10.
    Lawrence, S., Giles, C.L., Tsoi, A.C., Back, A.D.: Face recognition: a convolutional neural-network approach. IEEE Trans. Neural Netw. 8(1), 98–113 (2002)CrossRefGoogle Scholar
  11. 11.
    Li, H., Lin, Z., Shen, X., Brandt, J., Hua, G.: A convolutional neural network cascade for face detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5325–5334 (2015)Google Scholar
  12. 12.
    Tay, K.M., Lim, C.P.: Optimization of Gaussian fuzzy membership functions and evaluation of the monotonicity property of fuzzy inference systems. In: IEEE International Conference on Fuzzy Systems, pp. 1219–1224 (2011)Google Scholar
  13. 13.
    Van der Wilk, M., Rasmussen, C.E., Hensman, J.: Convolutional Gaussian processes. In: Advances in Neural Information Processing Systems, pp. 2849–2858 (2017)Google Scholar
  14. 14.
    Winkler, R., Klawonn, F., Kruse, R.: Fuzzy c-means in high dimensional spaces. Int. J. Fuzzy Syst. Appl. (IJFSA) 1(1), 1–16 (2011)CrossRefGoogle Scholar
  15. 15.
    Xiaodong, D., Zedong, L., Cunrui, W., Back, A.D.: Research on multi-ethnic face semantic description and mining method based on AFS. Chin. J. Comput. 39, 1435–1449 (2016)Google Scholar
  16. 16.
    Yongchuan, T., Yunsong, X.: Learning disjunctive concepts based on fuzzy semantic cell models through principles of justifiable granularity and maximum fuzzy entropy. Knowl.-Based Syst. 161, 268–293 (2018)CrossRefGoogle Scholar
  17. 17.
    Zeiler, M.D., Fergus, R.: Visualizing and understanding convolutional networks. In: European Conference on Computer Vision, pp. 818–833 (2014)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Dalian University of TechnologyDalianChina

Personalised recommendations