Advertisement

Semi-supervised Fuzzy Min–Max Neural Network for Data Classification

  • Jinhai Liu
  • Yanjuan MaEmail author
  • Fuming Qu
  • Dong Zang
Article
  • 49 Downloads

Abstract

Learning from the lack of labeled data is a challenging task which often limits the performance of the classifier. Since the unlabeled data is easy to obtain, using both of the labeled and unlabeled data in the training process provide a way to solve this problem. In this paper, a semi-supervised classification method based on fuzzy min–max neural network (SS-FMM) is proposed. In SS-FMM, the network has been modified for handling both of the labeled and unlabeled data. In addition, the staged feedback process is designed to modify the network structure of the traditional fuzzy min–max neural network. A staged-threshold function designed in SS-FMM, the hyperbox pruning process and the hyperbox relabeling process can be started dynamically. Moreover, the hyperboxes relabeling process and the hyperbox pruning process are designed to maximize using the unlabeled data and control the amount of the hyperboxes. In order to testify the effectiveness of SS-FMM, various experiments are carried out with several benchmark data sets. In addition, SS-FMM has been applied on the internal inspection data of our system. The results show that SS-FMMM has got good performance.

Keywords

Semi-supervised Fuzzy min–max neural network Data classification 

Notes

Acknowledgements

This work was supported by National Key R&D Program of China (2017YFF0108800) and the National Natural Science Foundation of China (61473069, 61627809).

References

  1. 1.
    Abaszade M, Effati S (2018) Stochastic support vector machine for classifying and regression of random variables. Neural Process Lett 48:1–29CrossRefGoogle Scholar
  2. 2.
    Ding S, Chen Z, Zhao S, Lin T (2018) Pruning the ensemble of ann based on decision tree induction. Neural Process Lett 48(1):53–70CrossRefGoogle Scholar
  3. 3.
    Zhou X, Belkin M (2014) Semi-supervised learning. 1(Supplement C):1239–1269 Google Scholar
  4. 4.
    Huang K, Zhang R, Yin X-C (2015) Learning imbalanced classifiers locally and globally with one-side probability machine. Neural Process Lett 41:311–323 CrossRefGoogle Scholar
  5. 5.
    Liu J, Fuming Q, Hong X, Zhang H (2018) A small-sample wind turbine fault detection method with synthetic fault data using generative adversarial nets. IEEE Trans Ind Inform 15(7):3877–3888 CrossRefGoogle Scholar
  6. 6.
    Das A, Pradhapan P, Groenendaal W, Adiraju P, Rajan RT, Catthoor F, Schaafsma S, Krichmar JL, Dutt N, Hoof CV (2018) Unsupervised heart-rate estimation in wearables with liquid states and a probabilistic readout. Neural Netw 99:134–147CrossRefGoogle Scholar
  7. 7.
    Zadeh LA (1965) Fuzzy sets. Inf Control 8(3):338–353CrossRefGoogle Scholar
  8. 8.
    Gath I, Geva AB (1989) Unsupervised optimal fuzzy clustering. IEEE Trans Pattern Anal Mach Intell 11(7):773–780CrossRefGoogle Scholar
  9. 9.
    Zhang H, Liu Z, Huang GB, Wang Z (2010) Novel weighting-delay-based stability criteria for recurrent neural networks with time-varying delay. IEEE Trans Neural Netw 21(1):91–106CrossRefGoogle Scholar
  10. 10.
    Zhang H, Ma T, Huang GB, Wang Z (2010) Robust global exponential synchronization of uncertain chaotic delayed neural networks via dual-stage impulsive control. IEEE Trans Syst Man Cybern B (Cybern) 40(3):831–844CrossRefGoogle Scholar
  11. 11.
    Sevgen S, Shekher V, Arik S, Ali MS, Narayanan G (2019) Global stability analysis of fractional-order fuzzy bam neural networks with time delay and impulsive effects. Commun Nonlinear Sci Numer Simul 78(1):104–853MathSciNetGoogle Scholar
  12. 12.
    Alsaedi A, Ahmad B, Ali MS, Vadivel R (2019) Extended dissipativity and event-triggered synchronization for TCS fuzzy markovian jumping delayed stochastic neural networks with leakage delays via fault-tolerant control. Soft Comput 1:1–20Google Scholar
  13. 13.
    Cao J, Lu G, Syed Ali M, Usha M (2019) Synchronisation analysis for stochastic tcs fuzzy complex networks with coupling delay. Int J Syst Sci 3(50):585–598Google Scholar
  14. 14.
    Simpson PK (1992) Fuzzy min–max neural networks. I. Classification. IEEE Trans Neural Netw 3(5):776–786CrossRefGoogle Scholar
  15. 15.
    Simpson PK (1993) Fuzzy min–max neural networks-part 2: clustering. IEEE Trans Fuzzy Syst 1(1):32CrossRefGoogle Scholar
  16. 16.
    Liu J, Ma Y, Zhang H, Hanguang S, Xiao G (2017) A modified fuzzy min–max neural network for data clustering and its application on pipeline internal inspection data. Neurocomputing 238:56–66CrossRefGoogle Scholar
  17. 17.
    Arribas JI, Cid-Sueiro J (2005) A model selection algorithm for a posteriori probability estimation with neural networks. IEEE Trans Neural Netw 16(4):799–809CrossRefGoogle Scholar
  18. 18.
    Seghouane A, Amari S (2007) The AIC criterion and symmetrizing the kullback–Leibler divergence. IEEE Trans Neural Netw 18(1):97–106CrossRefGoogle Scholar
  19. 19.
    Al Sayaydeh ON, Mohammed MF, Lim CP (2019) Survey of fuzzy min–max neural network for pattern classification variants and applications. IEEE Trans Fuzzy Syst 27(4):635–645CrossRefGoogle Scholar
  20. 20.
    Gabrys B, Bargiela A (2000) General fuzzy min–max neural network for clustering and classification. IEEE Trans Neural Netw 11(3):769–783CrossRefGoogle Scholar
  21. 21.
    Nandedkar AV, Biswas PK (2007) A fuzzy min–max neural network classifier with compensatory neuron architecture. IEEE Trans Neural Netw 18(1):42–54CrossRefGoogle Scholar
  22. 22.
    Nandedkar AV, Biswas PK (2009) A granular reflex fuzzy min–max neural network for classification. IEEE Trans Neural Netw 20(7):1117–1134CrossRefGoogle Scholar
  23. 23.
    Zhang H, Liu J, Ma D, Wang Z (2011) Data-core-based fuzzy min–max neural network for pattern classification. IEEE Trans Neural Netw 22(12):2339–2352CrossRefGoogle Scholar
  24. 24.
    Davtalab R, Dezfoulian MH, Mansoorizadeh M (2014) Multi-level fuzzy min–max neural network classifier. IEEE Trans Neural Netw Learn Syst 25(3):470–482CrossRefGoogle Scholar
  25. 25.
    Mirzamomen Z, Kangavari MR (2017) Evolving fuzzy min–max neural network based decision trees for data stream classification. Neural Process Lett 45(1):341–363CrossRefGoogle Scholar
  26. 26.
    Wu H, Prasad S (2017) Semi-supervised deep learning using pseudo labels for hyperspectral image classification. IEEE Trans Image Process 27:1259–1270MathSciNetCrossRefGoogle Scholar
  27. 27.
    Lichman M (2013) UCI machine learning repositoryGoogle Scholar
  28. 28.
    Mohammed MF, Lim CP (2015) An enhanced fuzzy min–max neural network for pattern classification. IEEE Trans Neural Netw Learn Syst 26(3):417–429MathSciNetCrossRefGoogle Scholar
  29. 29.
    Feng J, Li F, Lu S, Liu J, Ma D (2017) Injurious or noninjurious defect identification from MFL images in pipeline inspection using convolutional neural network. IEEE Trans Instrum Meas 66(7):1883–1892CrossRefGoogle Scholar
  30. 30.
    Ma Y, Liu J, Li T, Danyu L (2017) Staged-adaptive data clustering in fuzzy min–max neural network, pp 1–5Google Scholar
  31. 31.
    Liu J, Zang D, Liu C, Ma Y, Mingrui F (2019) A leak detection method for oil pipeline based on markov feature and two-stage decision scheme. Measurement 138:433–445CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.State Key Laboratory of Synthetical Automation for Process Industries, College of Information Science and EngineeringNortheastern UniversityShenyangChina

Personalised recommendations