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An extreme learning machine model based on adaptive multi-fusion chaotic sparrow search algorithm for regression and classification

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Abstract

Extreme learning machine (ELM) is a machine learning algorithm based on single hidden layer feedforward neural network. Compared with traditional neural network algorithms, ELM has the advantages of fast training speed and good generalization ability with guaranteed learning accuracy, which has been widely used in practical problems. However, due to the randomness of hidden layer parameters, the original ELM requires more hidden neurons in applications, making the network more complex. For this reason, we propose an improved extreme learning machine based on the adaptive multi-fusion chaotic sparrow search algorithm (AMFCSSA), called AMFCSSA-ELM. In our work, AMFCSSA is specially designed which combines the techniques of chaotic mapping, adaptive weighting coefficients, variable spiral factors and t-distribution mutation, to optimize the input weights and hidden biases of ELM. Firstly, unimodal and multimodal test functions are used to verify the performance of AMFCSSA, the results show that AMFCSSA can search for the global optimal value better than PSO, SSA, and so on. Secondly, various nonlinear functions and classification datasets are taken to test the capability of AMFCSSA-ELM, the simulation results show that our method has better generalization performance compared with other similar algorithms such as PSO-ELM, SSA-ELM.

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All data included in this study are available upon request by contact with the corresponding author.

References

  1. Ullah Z, Naqvi SR, Farooq W et al (2021) A comparative study of machine learning methods for bio-oil yield prediction–Agenetic algorithm-based features selection. Bioresource Technol 335:125292

    Article  Google Scholar 

  2. Jiménez AA, Zhang L, Muñoz CQG et al (2020) Maintenance management based on machine learning and nonlinear features in wind turbines. Renew Energ 146:316–328

    Article  Google Scholar 

  3. Kapukaranov B, Nakov P (2015) Fine-grained sentiment analysis for movie reviews in Bulgarian. In: Proceedings of the international conference recent advances in natural language processing, pp 266–274

  4. Pane C, Manganiello G, Nicastro N, Carotenuto F (2021) Early detection of wild rocket tracheofusariosis using hyperspectral image-based machine learning. Remote Sens-Basel 14(1):84

    Article  Google Scholar 

  5. Manifold B, Men SQ, Hu RQ, Fu D (2021) A versatile deep learning architecture for classification and label-free prediction of hyperspectral images. Nat Mach Intell 3(4):306–315

    Article  Google Scholar 

  6. Chehreghani MH (2021) Dynamical algorithms for data mining and machine learning over dynamic graphs. Wires Data Min Knowl 11(2):e1393

    Article  Google Scholar 

  7. Huang G B, Zhu QY, Siew CK (2004) Extreme learning machine: a new learning scheme of feedforward neural networks. In: 2004 IEEE international joint conference on neural networks. IEEE New York, pp 985–990

  8. Huang GB, Zhu QY, Siew CK (2006) Extreme learning mchine: theory and applications. Neurocomputing 70(1–3):489–501

    Article  Google Scholar 

  9. Huang GB (2003) Learning capability and storage capacity of two-hidden-layer feedforward networks. IEEE T Neural Networ 14(2):274–281

    Article  Google Scholar 

  10. Huang GB, Zhou HM, Ding XJ, Zhang R (2012) Extreme learning machine for regression and multiclass classification. IEEE Trans Syst Man Cybern Part B Cybern 42(2):513–529

    Article  Google Scholar 

  11. Gao Z, Hu QG, Xu XY (2021) Condition monitoring and life prediction of the turning tool based on extreme learning machine and transfer learning. Neural Comput Appl 34(5):3399–3410

    Article  Google Scholar 

  12. Khan MA, Kadry S, Parwekar P et al (2021) Human gait analysis for osteoarthritis prediction: a framework of deep learning and kernel extreme learning machine. Complex Intell Syst 19:1–19

    Google Scholar 

  13. Su XL, Sun SL, Zhang S, Yin YX, Xiao WD (2020) Improved multi-layer online sequential extreme learning machine and its application for hot metal silicon content. J Franklin I 357(17):12588–12608

    Article  Google Scholar 

  14. Zhang HG, Zhang S, Yin YX, Chen XZ (2018) Prediction of the hot metal silicon content in blast furnace based on extreme learning machine. Int J Mach Learn Cyber 9(10):1697–1706

    Article  Google Scholar 

  15. Kalpana V, Maheswar R, Nandakumar E (2020) Multiple parametric fault diagnosis using computational intelligence techniques in linear filter circuit. J Amb Intell Humaniz Comput 11(11):5533–5545

    Article  Google Scholar 

  16. Murugan R, Goel T (2021) E-DiCoNet: Extreme learning machine based classifier for diagnosis of COVID-19 using deep convolutional network. J Amb Intel Hum Comp 12(9):8887–8898

    Article  Google Scholar 

  17. Chen ZY, Gryllias K, Li WH (2019) Mechanical fault diagnosis using convolutional neural networks and extreme learning machine. Mech Syst Signal Pr 133:106272

    Article  Google Scholar 

  18. Grissette H, Nfaoui EH (2021) Affective concept-based encoding of patient narratives via sentic computing and neural networks. Cogn Comput 14:274

    Article  Google Scholar 

  19. Xia JF, Yang DQ, Zhou H et al (2022) Evolving kernel extreme learning machine for medical diagnosis via a disperse foraging sine cosine algorithm. Comput Biol Med 141:105137

    Article  Google Scholar 

  20. Han F, Yao HF, Ling QH (2013) An improved evolutionary extreme learning machine based on particle swarm optimization. Neurocomputing 116:87–93

    Article  Google Scholar 

  21. Zhu QY, Qin AK, Suganthan PN, Huang GB (2005) Evolutionary extreme learning machine. Pattern Recogn 38(10):1759–1763

    Article  Google Scholar 

  22. Alshamiri AK, Singh A, Surampudi BR (2018) Two swarm intelligence approaches for tuning extreme learning machine. Int J Mach Learn Cyber 9(8):1271–1283

    Article  Google Scholar 

  23. Xue JK, Shen B (2020) A novel swarm intelligence optimization approach: sparrow search algorithm. Syst Sci Control Eng 8(1):22–34

    Article  Google Scholar 

  24. Yang XX, Liu J, Liu Y et al (2021) A novel adaptive sparrow search algorithm based on chaotic mapping and t-distribution mutation. Appl Sci 11(23):11192

    Article  Google Scholar 

  25. Ouyang CT, Qiu YX (1848) Zhu DL (2021) A multi-strategy improved sparrow search algorithm. J Phys Conf Ser 1:012042

    Google Scholar 

  26. Abdulhammed OY (2021) Load balancing of IoT tasks in the cloud computing by using sparrow search algorithm. J Supercomput 78:3266

    Article  Google Scholar 

  27. Li XY, Li SB, Zhou P, Chen GL (2022) Forecasting network interface flow using a broad learning system based on the sparrow search algorithm. Entropy 24(4):478

    Article  MathSciNet  Google Scholar 

  28. Ouyang CT, Zhu DL, Qiu YX (2021) Lens learning sparrow search algorithm. Math Probl Eng

  29. Yan SQ, Yang P, Zhu DL, Zheng WL, Wu FX (2021) Improved Sparrow Search Algorithm Based on Iterative Local Search. Comput Intel Neurosc /. https://doi.org/10.1155/2021/6860503

    Article  Google Scholar 

  30. Ouyang CT, Zhu DL, Wang FQ (2021) A learning sparrow search algorithm. Comput Intel Neurosc. https://doi.org/10.1155/2021/3946958

    Article  Google Scholar 

  31. Saremi S, Mirjalili S, Lewis A (2014) Biogeography-based optimisation with chaos. Neural Comput Appl 25(5):1077–1097

    Article  Google Scholar 

  32. Ouyang CT, Qiu YX, Zhu DL (2021) Adaptive spiral flying sparrow search algorithm. Sci Programming-Neth. https://doi.org/10.1155/2021/6505253

    Article  Google Scholar 

  33. Liu WB, Wang ZD, Yuan Y, Zeng NY, Hone K, Liu XH (2019) A novel sigmoid-function-based adaptive weighted particle swarm optimizer. IEEE T Cybernetics 51(2):1085–1093

    Article  Google Scholar 

  34. Liang XL, Li WF, Zhang Y, Zhou MC (2015) An adaptive particle swarm optimization method based on clustering. Soft Comput 19(2):431–448

    Article  Google Scholar 

  35. Mirjalili S, Lewis A (2016) The whale optimization algorithm. Adv Eng Softw 95:51–67

    Article  Google Scholar 

  36. Gharehchopogh FS, Gholizadeh H (2019) A comprehensive survey: whale optimization algorithm and its applications. Swarm Evol Comput 48:1–24

    Article  Google Scholar 

  37. Zhang CL, Ding SF (2021) A stochastic configuration network based on chaotic sparrow search algorithm. Knowl-Based Syst 220:106924

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant Nos. 11601411), the Natural Science Basic Research Plan in Shaanxi Province of China (Program No. 2021JM-448), the Natural Science Basic Research Plan in Shaanxi Province of China (Program No. 2023-JC-YB-063)

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  1. School of Science, Xi’an Polytechnic University, Xi’an, 710048, China

    Ruibo Zhang, Jin Su & Jinqian Feng

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  1. Ruibo Zhang
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  2. Jin Su
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All the authors have contributed to introduction, literature, conceptualization, methodology, writing and conclusion.

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Correspondence to Jin Su.

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Zhang, R., Su, J. & Feng, J. An extreme learning machine model based on adaptive multi-fusion chaotic sparrow search algorithm for regression and classification. Evol. Intel. 17, 1567–1586 (2024). https://doi.org/10.1007/s12065-023-00852-0

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