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Evolved fuzzy min-max neural network for new-labeled data classification

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Abstract

Pattern classification is a fundamental problem in many data-driven application domains. New-labeled data refers to the data with the labels that are new and different from source labels. How to learn the new-labeled data is a crucial research in the data classification. In this paper, an evolved fuzzy min-max neural network for new-labeled data classification (FMM-NLA) is proposed. In FMM-NLA, the network can be self-evolved. Unlike the traditional FMM methods, the trained network of FMM-NLA can be expanded when new-labeled data added. FMM-NLA is a continuing-learning method, which can realize the continuing training process without retraining all the data. In order to verify the superiority of the proposed method, benchmark data sets are used. The experimental results show that FMM-NLA is effective in handling new-labeled data. Moreover, the application result on the pipeline defect recognition in depth shows that FMM-NLA is effective in solving the new-labeled defect recognition problem.

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References

  1. Khari M, Garg AK, Gonzalez-Crespo R, Verd E (2019) Gesture Recognition of RGB and RGB-D Static Images Using Convolutional Neural Networks. Int J Interact Multimed Artif Intell 5(7):22–27. https://doi.org/10.9781/ijimai.2019.09.002

  2. Bouchra N, Aouatif A, Mohammed N, Nabil H (2019) Auto-Encoder For face classification deep belief Network and international. J Interact Multimed Artif Intell 5(5):22–29. https://doi.org/10.9781/ijimai.2018.06.004

    Google Scholar 

  3. Kumar NA, Sanjay C, Chakravarthy M (2019) Mamdani Fuzzy Expert System Based Directional Relaying Approach for Six-Phase Transmission Line. International Journal of Interactive Multimedia and Artificial Intelligence. In: Press:1-11. https://doi.org/10.9781/ijimai.2019.06.002

  4. Jain AK (1999) Data clustering: a review[J]. Acm Comput Surv 31(3):264–323

    Article  Google Scholar 

  5. Chen Y, Lin Z, Zhao X et al (2014) Deep Learning-Based classification of hyperspectral Data[J]. IEEE J Sel Top Appl Earth Observ Remote Sens 7(6):2094–2107

    Article  Google Scholar 

  6. Kim HM, Choi DH (2016) Defects detection of gas pipeline near the welds based on self quotient image and discrete cosine transform. Russ J Nondestruct Test 52(3): 175C183

  7. Liu J, Zang D, Liu C, Ma Y, Fu M (2019) A leak detection method for oil pipeline based on markov feature and two-stage decision scheme. Measurement 138:433-445, ISSN:0263-2241. https://doi.org/10.1016/j.measurement.2019.01.029

  8. Antipov AG, Markov AA (2018) 3D simulation and experiment on high speed rail MFL inspection. NDT E Int 98:177–185, ISSN:0963-8695. https://doi.org/10.1016/j.ndteint.2018.04.011

  9. Wu D, Liu Z, Wang X, Su L (2017) Composite magnetic flux leakage detection method for pipelines using alternating magnetic field excitation. NDT E Int 91:148–155. ISSN:0963-8695, https://doi.org/10.1016/j.ndteint.2017.07.002

  10. Han W, Wu Z, Zhou M, et al. (2017) Magnetic flux leakage signal inversion based on improved efficient population utilization strategy for particle swarm Optimization[J]. Russ J Nondestruct Test 53(12):862–873

    Article  Google Scholar 

  11. Polikar R, et al. (2001) Learn++: an incremental learning algorithm for supervised neural networks. IEEE Trans Syst Man Cybern Part C (Appl Rev) 31(4):497–508

    Article  Google Scholar 

  12. Ahmed AA, Mohammed MF (2018) SAIRF: A similarity approach for attack intention recognition using fuzzy min-max neural network. J Comput Sci 25:467–473. ISSN:1877-7503, https://doi.org/10.1016/j.jocs.2017.09.007

  13. Jawarkar NP (2007) Emotion Recognition using Prosody Features and a Fuzzy Min-Max Neural Classifier[J]. IETE Tech Rev 24(5):369–373

    Google Scholar 

  14. Schlimmer JC, Granger RH (1986) Incremental learning from noisy data. Mach Learn 1 (3):317–354

    Google Scholar 

  15. Bishop CM (2006) Pattern recognition and machine learning (information science and Statistics)[M]. Springer, New York

  16. Al Sayaydeh ON, Mohammed MF, Lim CP (2019) Survey of Fuzzy minCMax Neural Network for Pattern Classification Variants and Applications. IEEE Trans Fuzzy Syst 27(4):635–645. https://doi.org/10.1109/TFUZZ.2018.2865950

    Article  Google Scholar 

  17. Shinde S, Kulkarni U (2017) Extended fuzzy hyperline-segment neural network with classification rule extraction. Neurocomputing 260(oct.18):79–91, ISSN:0925-2312, https://doi.org/10.1016/j.neucom.2017.03.036

  18. Simpson PK (1992) Fuzzy min-max neural networks. I. Classification. IEEE Trans Neural Netw 3(5):776–786. https://doi.org/10.1109/72.159066

    Article  Google Scholar 

  19. Simpson PK (1993) Fuzzy min-max neural networks - Part 2: Clustering. IEEE Trans Fuzzy Syst 1(1):32-. https://doi.org/10.1109/TFUZZ.1993.390282

  20. Gabrys B, Bargiela A (2000) General fuzzy min-max neural network for clustering and classification[M]. IEEE Press

  21. Mohammed MF, Lim CP (2016) Improving the Fuzzy Min-Max Neural Network with a K-nearest Hyperbox Expansion Rule for Pattern Classification[J]. Appl Soft Comput, pp 52

  22. Khuat TT, Chen F (2019) Gabrys B. An Effective Multi-Resolution Hierarchical Granular Representation based Classifier using General Fuzzy Min-Max Neural Network[J]. IEEE Trans Fuzzy Syst PP(99):1–1

  23. Liu J, Qu Y, Dong FZ (2019) Semi-supervised Fuzzy Min–Max Neural Network for Data Classification[J]. Neural Process Lett (Nov.14):1573-773X

  24. Bezdek JC, Ehrlich R, Full WFCM (1984) The fuzzy c -means clustering algorithm[J]. Comput Geosci 10(2):191– 203

    Article  Google Scholar 

  25. Liu J, Ma Y, Zhang H, Su H, Xiao G (2017) A modified fuzzy minCmax neural network for data clustering and its application on pipeline internal inspection data. Neurocomputing 238:56–66. ISSN:0925-2312, https://doi.org/10.1016/j.neucom.2017.01.036

  26. Quteishat AM, Lim CP (2007) A modified fuzzy min-max neural network and its application to fault classification[M]. Soft Computing in Industrial Applications Springer Berlin Heidelberg

  27. Zhang H, Liu J, Ma D, Wang Z (2011) Data-Core-Based Fuzzy Min-Max neural network for pattern Classification[J]. IEEE Trans Neural Netw 22(12):2339–2352. https://doi.org/10.1109/TNN.2011.2175748

    Article  Google Scholar 

  28. Dua D, Graff C (2019) UCI Machine Learning Repository http://archive.ics.uci.edu/ml. University of California, School of Information and Computer Science, Irvine

  29. Carvalho AA, Rebello JMA, Sagrilo LVS, et al. (2006) MFL Signals and artificial neural networks applied to detection and classification of pipe weld defects[J]. NDT E Int 39(8):661– 667

    Article  Google Scholar 

  30. Xu J, et al. (2019) Low-Cost, Tiny-Sized MEMS Hydrophone Sensor for Water Pipeline Leak Detection. IEEE Trans Ind Electron 66(8):6374–6382. https://doi.org/10.1109/TIE.2018.2874583

    Article  Google Scholar 

  31. Qidwai UA (2009) Autonomous corrosion detection in gas pipelines: a hybrid-fuzzy classifier approach using ultrasonic nondestructive evaluation protocols. IEEE Trans Ultrason Ferroelectr Frequen Control 56 (12):2650–2665. https://doi.org/10.1109/TUFFC.2009.1356

    Article  Google Scholar 

  32. Lu S, Feng J, Zhang H, Liu J, Wu Z (2019) An estimation method of defect size from MFL image using visual transformation convolutional neural network. IEEE Trans Ind Inf 15(1):213–224. https://doi.org/10.1109/TII.2018.2828811

    Article  Google Scholar 

  33. Liu J, Qu F, Hong X, Zhang H A Small-sample Wind Turbine Fault Detection Method with Synthetic Fault Data. Using Generative Adversarial Nets. IEEE Trans Ind Inf. https://doi.org/10.1109/TII.2018.2885365

  34. Rostami Kandroodi M, Nadjar Araabi B, Mansoob Bassiri M, Nili Ahmadabadi M (2017) Estimation of depth and length of defects from magnetic flux leakage measurements: Verification with simulations, experiments and pigging data. IEEE Trans Magn 53(3):1–10. https://doi.org/10.1109/TMAG.2016.2631525

    Google Scholar 

Download references

Acknowledgements

This work was supported by National Key R&D Program of China (2017YFF0108800), the National Natural Science Foundation of China (61973071, 61627809, 61703087), the Liaoning Natural Science Foundation of China (2019 − KF − 03 − 04), the Liaoning Revitalization Talents Program(XLYC1907138).

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Authors and Affiliations

  1. Key Laboratory of Regional Multi-energy System Integration and Control of Liaoning Province, School of Renewable Energy, Shenyang Institute of Engineering, Shenyang, 110136, China

    Yanjuan Ma

  2. State Key Laboratory of Synthetical Automation for Process Industries, College of Information Science and Engineering, Northeastern University, Shenyang, 110819, China

    Jinhai Liu, Fuming Qu & Hongfei Zhu

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  1. Yanjuan Ma
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  2. Jinhai Liu
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  3. Fuming Qu
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  4. Hongfei Zhu
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Correspondence to Yanjuan Ma or Jinhai Liu.

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Ma, Y., Liu, J., Qu, F. et al. Evolved fuzzy min-max neural network for new-labeled data classification. Appl Intell 52, 305–320 (2022). https://doi.org/10.1007/s10489-021-02259-9

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