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Self-updating continual learning classification method based on artificial immune system

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Abstract

Currently, major classification methods belong to batch learning methods, which need to obtain all data once before learning. However, in practice, it is usually difficult to handle all samples at once for samples are obtained gradually at different periods. Unfortunately, research on continual learning study is deficiency and remains to be improved. In this work, a self-updating continual learning classification method (SU-CLCM) is proposed, according to the sophisticated continual learning mechanism of biological immune system. In SU-CLCM, the self-updating cell division strategy overcomes the irrational cell strategies; SU-CLCM takes the advantage of super memory cells and totipotent stem cells with self-updating cell weight so that different types of cell edge are more distinguishable; SU-CLCM can improve the result of high-dimensional data processing. Experiments demonstrated on 20 UCI repository datasets compared with intelligent classification methods and artificial immune methods with continual learning ability to corroborate the highlights of SU-CLCM. Ultimately, a dataset of actual compressor valve fault is employed to verify the effectiveness and superiority of the proposed method.

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Data availability

https://doi.org/10.24433/CO.4245521.v1

Code availability

https://doi.org/10.24433/CO.4245521.v1

References

  1. Michalski RS (1986) The multi-purpose incremental learning system AQ15 and its testing application to three medical domains. In: Proceedings of the fifth national conference on artificial intelligence

  2. Li Z, Hoiem D (2017) Learning without Forgetting. IEEE Trans Pattern Anal Mach Intell 40(12):2935–2947. https://doi.org/10.1109/TPAMI.2017.2773081

    Article  Google Scholar 

  3. Rebuffi SA, Kolesnikov A, Sperl G, Lampert CH (2016) iCaRL: incremental Classifier and Representation Learning. IEEE Comp Soc 1:5533–5542. https://doi.org/10.1109/CVPR.2017.587

    Article  Google Scholar 

  4. Shen F, Ouyang Q, Kasai W, Hasegawa O (2013) A general associative memory based on self-organizing incremental neural network. Neurocomputing 104(Mar.15):57–71. https://doi.org/10.1016/j.neucom.2012.10.003

    Article  Google Scholar 

  5. Liu D, Cong M, Du Y (2017) Episodic memory-based robotic planning under uncertainty. IEEE Trans Ind Electron 64(2):1762–1772. https://doi.org/10.1109/TIE.2016.2613507

    Article  Google Scholar 

  6. Abid A, Khan MT, Silva CD (2017) Layered and real-valued negative selection algorithm for fault detection. IEEE Syst J 12:2960–2969. https://doi.org/10.1109/JSYST.2017.2753851

    Article  Google Scholar 

  7. Li D, Sun X, Gao F, Liu S (2021) An improved real-valued negative selection algorithm based on the constant detector for anomaly detection. J Intell Fuzzy Syst 40:8793–8806. https://doi.org/10.3233/JIFS-200405

    Article  Google Scholar 

  8. Li D, Liu S, Zhang H (2017) A method of anomaly detection and fault diagnosis with online adaptive learning under small training samples. Pattern Recogn 64:374–385. https://doi.org/10.1016/j.patcog.2016.11.026

    Article  Google Scholar 

  9. Dasgupta D, Yu S, Nino F (2011) Recent advances in artificial immune systems: models and applications. Appl Soft Comput 11(2):1574–1587. https://doi.org/10.1016/j.asoc.2010.08.024

    Article  Google Scholar 

  10. Claus L, Ole L, Can K, Sren B, Morten N (2007) Modeling the adaptive immune system: predictions and simulations. Bioinformatics 23:3265–3275. https://doi.org/10.1093/bioinformatics/btm471

    Article  Google Scholar 

  11. Kidd R (2019) Artificial immune systems: an overview for faulting actuators. Actuators 8(3):53. https://doi.org/10.3390/act8030053

    Article  Google Scholar 

  12. Bayar N, Darmoul S, Hajri-Gabouj S, Pierreval H (2015) Fault detection, diagnosis and recovery using artificial immune systems: a review. Eng Appl Artif Intell 46(NOV.PT.A):43–57. https://doi.org/10.1016/j.engappai.2015.08.006

    Article  Google Scholar 

  13. Ji Z, Dasgupta D (2009) V-detector: an efficient negative selection algorithm with "probably adequate" detector coverage. Inf Sci 179(10):1390–1406. https://doi.org/10.1016/j.ins.2008.12.015

    Article  Google Scholar 

  14. Nan X, Yongsheng D, Lihong R et al (2018) Degeneration recognizing clonal selection algorithm formultimodal optimization[J]. IEEE Trans Cybernetics 48(3):848–861

    Article  Google Scholar 

  15. Castro L, Zuben F (2000) An evolutionary immune network for data clustering. In: Brazilian symposium on neural networks IEEE, pp 84–89.

  16. Watkins A, Timmis J, Boggess L (2004) Artificial immune recognition system (airs): an immune-inspired supervised learning algorithm. Genet Program Evolvable Mach 5(1):307–318. https://doi.org/10.1023/B:GENP.0000030197.83685.94

    Article  Google Scholar 

  17. Cooper MD, Alder MN (2006) The evolution of adaptive immune systems. Cell 124(4):815–822. https://doi.org/10.1016/j.cell.2006.02.001

    Article  Google Scholar 

  18. Tao Y, Wen C, Tao L, Tao Y, Wen C, Tao L et al (2017) An antigen space density based real-value negative selection algorithm. Appl Soft Comput 61:860–874. https://doi.org/10.1016/j.asoc.2017.09.005

    Article  Google Scholar 

  19. Castro L, Zuben F (2001) The clonal selection algorithm with engineering applications

  20. Li D, Liu S, Gao F, Sun X (2021) Continual learning classification method and its application to equipment fault diagnosis. Appl Intell 1. https://doi.org/10.1007/s10489-021-02455-7

  21. Li D, Liu S, Gao F, Sun X (2020) Continual learning classification method with new labeled data based on the artificial immune system. Appl Soft Comput 94:106423. https://doi.org/10.1016/j.asoc.2020.106423

    Article  Google Scholar 

  22. Li D, Liu S, Gao F, Sun X (2021) Continual learning classification method with constant-sized memory cells based on the artificial immune system. Knowl-Based Syst 213:106673. https://doi.org/10.1016/j.knosys.2020.106673

    Article  Google Scholar 

  23. Jenhani I, Elouedi Z (2014) Re-visiting the artificial immune recognition system: a survey and an improved version. Artif Intell Rev 42(4):821–833. https://doi.org/10.1007/s10462-012-9360-0

    Article  Google Scholar 

  24. Yasmine S, Hassiba N, Youcef C (2016) New off-line handwritten signature verification method based on artificial immune recognition system. Expert Syst Appl 51(C):186–194. https://doi.org/10.1016/j.eswa.2016.01.001

    Article  Google Scholar 

  25. Medzhitov R (2007) Recognition of microorganisms and activation of the immune response. Nature 449:819–826. https://doi.org/10.1038/nature06246

    Article  Google Scholar 

  26. Der Maaten LV, Hinton GE (2008) Visualizing data using t-SNE[J]. J Mach Learn Res 9(86):2579–2605. https://doi.org/10.1007/s10846-008-9235-4

    Article  MATH  Google Scholar 

  27. Dua D, Graff C, UCI machine learning repository. http://archive.ics.uci.edu/ml

  28. Miche Y, van Heeswijk M, Bas P, Simula O, Lendasse A (2011) TROP-ELM: a double-regularized ELM using LARS and Tikhonov regularization. Neurocomputing 74:2413–2421. https://doi.org/10.1016/j.neucom.2010.12.042

    Article  Google Scholar 

  29. Shang R, Zhang W, Li F, Jiao L, Stolkin R (2019) Multi-objective artificial immune algorithm for fuzzy clustering based on multiple kernels. Swarm Evol Comput. https://doi.org/10.1016/j.swevo.2019.01.001

  30. Sun X, Zhou H (2010) Experiments with two new boosting algorithms. Intell Inf Manag 2:386–390. https://doi.org/10.4236/iim.2010.26047

    Article  Google Scholar 

  31. Huerta R, Vembu S, Amigó JM, Nowotny T, Elkan C (2012) Inhibition in multiclass classification. Neural Comput 24(9):2473–2507. https://doi.org/10.1162/NECO_a_00321

    Article  MathSciNet  MATH  Google Scholar 

  32. Mirzaei B, Nikpour B, Nezamabadi-Pour H (2021) CDBH: A clustering and density-based hybrid approach for imbalanced data classification[J]. Expert Syst Appl 164:114035. https://doi.org/10.1016/j.eswa.2020.114035

    Article  Google Scholar 

  33. Atif SM, Khan S, Naseem I, et al (2020) Multi-kernel fusion for RBF neural networks. https://arxiv.org/pdf/2007.02592.pdf

  34. Zhao DF, Zhang HL, Liu SL, Wei Y, Xiao SG (2021) Deep rational attention network with threshold strategy embedded for mechanical fault diagnosis. IEEE Trans Instrum Meas 70. https://doi.org/10.1109/TIM.2021.3085951

  35. Xiao S, Liu S, Song M, Nie A, Zhang H (2020) Coupling rub-impact dynamics of double translational joints with subsidence for time-varying load in a planar mechanical system. Multibody Sys Dyn 48(4):451–486. https://doi.org/10.1007/s11044-019-09718-9

    Article  MathSciNet  MATH  Google Scholar 

  36. Xiao S, Liu S, Song M, Nie A, Zhang H (2020) Coupling rub-impact dynamics of double translational joints with subsidence for time-varying load in a planar mechanical system. Multibody Sys Dyn 48(4):451–486

    Article  MathSciNet  Google Scholar 

  37. Zhao DF, Liu SL, Zhang HL, Sun X, Wang L, Wei Y (2021) Intelligent fault diagnosis of reciprocating compressor based on attention mechanism assisted convolutional neural network via vibration signal rearrangement. Arab J Sci Eng. https://doi.org/10.1007/s13369-021-05515-9

  38. Li S, Chen H, Wang M, Heidari AA, Mirjalili S (2020) Slime mould algorithm: a new method for stochastic optimization. Futur Gener Comput Syst 111:300–323. https://doi.org/10.1016/j.future.2020.03.055

    Article  Google Scholar 

  39. Yang Y, Chen H, Heidari AA, Gandomi AH (2021) Hunger games search: visions, conception, implementation, deep analysis, perspectives, and towards performance shifts. Expert Syst Appl 177:114864. https://doi.org/10.1016/j.eswa.2021.114864

    Article  Google Scholar 

  40. Heidari AA, Mirjalili S, Faris H, Aljarah I, Mafarja M, Chen H (2019) Harris hawks optimization: algorithm and applications. Futur Gener Comput Syst 97:849–872

    Article  Google Scholar 

  41. Wang G-G (2018) Moth search algorithm: a bio-inspired metaheuristic algorithm for global optimization problems. Memetic Comp 10:151–164. https://doi.org/10.1007/s12293-016-0212-3

    Article  Google Scholar 

  42. Alweshah M, Al Khalaileh S, Gupta BB, Almomani A, Hammouri AI, Al-Betar MAThe monarch butterfly optimization algorithm for solving feature selection problems. Neural Comput & Applic. https://doi.org/10.1007/s00521-020-05210-0

  43. Jerne NK (1974) Towards a network theory of the immune system[J]. Ann Immunol Paris 125C:373–389

    Google Scholar 

  44. Farmer JD, Packard NH, Perelson AS (1986) The immune system, adaptation, and machine learning[J]. Physica D 2(1-3):187–204

    Article  MathSciNet  Google Scholar 

  45. Ishida Y (1990) Fully distributed diagnosis by PDP learning algorithm: towards immune network PDP model[C]. In: IEEE international joint conference on neural networks, San Diego, 1990

  46. Ishida Y (1996) An immune network approach to sensor-based diagnosis by self-organization [J]. Compl Syst 10:73–90

    Google Scholar 

  47. Hunt JE, Cooke DE (1996) Learning using an artificial immune system[J]. J Netw Comput Appl 19:189–212

    Article  Google Scholar 

  48. Gao XZ, Ovaska SJ, Wang X, Chow M-Y (2006) Clonal optimization of negative selection algorithm with applications in motor fault detection[C]. In: IEEE international conference on Systems, Man and Cybernetics (SMC’06), Taipei, 2006

  49. Castro LND, Zuben FJV (2002) Learning and optimization using the clonal selection principle[J]. IEEE Trans Evol Comput 6:239–251

    Article  Google Scholar 

  50. De Castro LN, Von Zuben FJ (2001) Immune and neural network models: theoretial and empirical comparisons[J]. Int J Comput Intell Appl 1(3):239–257

    Article  Google Scholar 

  51. Masutti TAS, De Castro LN (2009) A self-organizing neural network using ideas from the immune system to solve the traveling salesman problem[J]. Inf Sci 179(10):1454–1468

    Article  MathSciNet  Google Scholar 

  52. Masutti TAS, De Castro LN (2009) Neuro-immune approach to solve routing problems[J]. Neurocomputing 72(10-12):2189–2197

    Article  Google Scholar 

  53. Ciccazzo A, Conca P, Nicosia G, Stracquadanio G (2008) An advanced clonal selection algorithm with ad hoc network-based hypermutation operators for synthesis of topology and sizing of analog electrical circuits[C]. In: 7th international conference on artificial immune systems, Phuket, Thailand, 2008

  54. Forrest S, Perelson AS, Allen L, Cherukuri R (1994) Self-nonself discrimination in a computer[J]. In: Proceedings of IEEE symposium on computer security and privacy, Oakland, CA, May 16–18, 1994, 5: 202–212

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Funding

This work was sponsored by the National Natural Science Foundation of China (Grant No. 52075310).

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Authors and Affiliations

  1. School of Mechatronics Engineering and Automation, Shanghai University, Shanghai, 200072, Peoples, Republic of China

    Xin Sun, Haotian Wang, Shulin Liu & Haihua Xiao

  2. School of Petroleum Engineering, Changzhou University, Changzhou, 213164, Peoples, Republic of China

    Dong Li

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  1. Xin Sun
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  2. Haotian Wang
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  3. Shulin Liu
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  4. Dong Li
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Contributions

Xin Sun: Methodology, Conceptualization, Writing – original draft. HaoTian Wang: Software, Writing – original draft. Shulin Liu: Funding acquisition, Writing – review& editing, Validation. Dong Li: Supervision, Software, Writing – review& editing, Validation. Haihua Xiao: Validation, Writing – review& editing.

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Correspondence to Haotian Wang.

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Sun, X., Wang, H., Liu, S. et al. Self-updating continual learning classification method based on artificial immune system. Appl Intell 52, 12817–12843 (2022). https://doi.org/10.1007/s10489-021-03123-6

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