Skip to main content
Springer Nature Link
Log in

Improved optimization parameters prediction using the modified mega trend diffusion function for a small dataset problem

  • Regular Paper
  • Published:
Knowledge and Information Systems Aims and scope Submit manuscript

Abstract

This paper proposes a modified mega trend diffusion (MTD) function based on the K-means clustering algorithm to generate artificial samples for a training dataset. This would improve the prediction accuracy in a backpropagation neural network (BPNN) algorithm used in small dataset problems. The main contribution of this paper is in solving the attributes redundancy problem in an mega trend diffusion (MTD) function construction when there are two and three overlapped regions in the functions, using the K-means clustering algorithm. When used in predicting the parameters of an optimization algorithm, significant improvements in the prediction errors were observed, compared to the previous MTD method. The improvements were achieved by clustering the membership function (MF) for each attribute from the overlapped regions in the MF triangle. In this work, this algorithm is used to predict the control parameters of the artificial bee colony optimization (ABCO) (\({N}_{i}\) and \({L}_{i}\)), which was then used in finding the optimal exit door locations of building layouts. For a case study, six samples of multi-room building layouts were considered. Each layout consists of information on the number of rooms (\({n}_{i}\)), room sizes (\({s}_{i}\)) and corridor width (\({w}_{i}\)). The performance of the model was evaluated against the conventional MTD method. The superiority of the proposed method over the conventional MTD was confirmed by the 17.67% and 28.68% improvements in the prediction error for twofold and threefold cross-validations, respectively. It is envisaged that the method can be very useful in improving the prediction error of data samples of various scales and with different sizes of artificial data.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14

Similar content being viewed by others

References

  1. Xie Y, Wang T, Xing Z, Huan H, Zhang Y, Li Y (2022) An improved indoor location algorithm based on backpropagation neural network. Arab J Sci Eng pp 1–13

  2. Zhu B, Ye S, Wang P, Chevallier J, Wei Y-M (2022) Forecasting carbon price using a multi-objective least squares support vector machine with mixture kernels. J Forecast 41(1):100–117

    Article  MathSciNet  Google Scholar 

  3. Wood DA (2022) Dataset insight and variable influences established using correlations, regressions, and transparent customized formula optimization. In: Sustainable geoscience for natural gas subsurface systems, Elsevier, pp 383–408

  4. Cui Z, Xue F, Cai X, Cao Y, Wang G, Chen J (2018) Detection of malicious code variants based on deep learning. IEEE Trans Ind Informatics 14(7):3187–3196

    Article  Google Scholar 

  5. Wang G, Guo L, Duan H (2013) Wavelet neural network using multiple wavelet functions in target threat assessment. Sci World J, vol 2013

  6. Wang G-G, Lu M, Dong Y-Q, Zhao X-J (2016) Self-adaptive extreme learning machine. Neural Comput Appl 27(2):291–303

    Article  Google Scholar 

  7. Yi J-H, Wang J, Wang G-G (2016) Improved probabilistic neural networks with self-adaptive strategies for transformer fault diagnosis problem. Adv Mech Eng 8(1):1687814015624832

    Article  Google Scholar 

  8. Niyogi P, Girosi F, Poggio T (1998) Incorporating prior information in machine learning by creating virtual examples. Proc IEEE. https://doi.org/10.1109/5.726787

    Article  Google Scholar 

  9. Abdul Lateh M, Muda AK, Izzah Mohd Yusof Z, Azilah Muda N, Sanusi Azmi M (2017) Handling a small dataset problem in prediction model by employ artificial data generation approach: a review. https://doi.org/10.1088/1742-6596/892/1/012016

  10. Huang C, Moraga C (2004) A diffusion-neural-network for learning from small samples. Int J Approx Reason. https://doi.org/10.1016/j.ijar.2003.06.001

    Article  MathSciNet  MATH  Google Scholar 

  11. Li D-C, Wu C-S, Tsai T-I, Lina Y-S (2007) Using mega-trend-diffusion and artificial samples in small data set learning for early flexible manufacturing system scheduling knowledge. Comput Oper Res 34(4):966–982. https://doi.org/10.1016/j.cor.2005.05.019

    Article  MATH  Google Scholar 

  12. Li DC, Wen IH (2014) A genetic algorithm-based virtual sample generation technique to improve small data set learning. Neurocomputing. https://doi.org/10.1016/j.neucom.2014.06.004

    Article  Google Scholar 

  13. Chen ZS, Zhu B, He YL, Yu LA (2017) A PSO based virtual sample generation method for small sample sets: applications to regression datasets. Eng Appl Artif Intell 59:236–243. https://doi.org/10.1016/j.engappai.2016.12.024

    Article  Google Scholar 

  14. Lin LS, Li DC, Pan CW (2016) Improving virtual sample generation for small sample learning with dependent attributes. https://doi.org/10.1109/IIAI-AAI.2016.18

  15. Li DC, Chang CC, Liu CW, Chen WC (2013) A new approach for manufacturing forecast problems with insufficient data: the case of TFT-LCDs. J Intell Manuf 24(2):225–233. https://doi.org/10.1007/s10845-011-0577-6

    Article  Google Scholar 

  16. Li DC, Liu CW (2012) Extending attribute information for small data set classification. IEEE Trans Knowl Data Eng. https://doi.org/10.1109/TKDE.2010.254

    Article  Google Scholar 

  17. Kuligowski ED, Peacock RD (2005) A Review of Building Evacuation Models. Natl Inst Stand Technol 1471:156

    Google Scholar 

  18. Li Q, Fang Z, Li Q, Zong X (2010) Multiobjective evacuation route assignment model based on genetic algorithm. In: 2010 18th international conference on geoinformatics, geoinformatics 2010, pp 1–5. https://doi.org/10.1109/GEOINFORMATICS.2010.5567485

  19. Han LD, Yuan F, Chin SM, Hwang H (2006) Global optimization of emergency evacuation assignments. Interfaces (Providence) 36(6):502–513. https://doi.org/10.1287/inte.1060.0251

    Article  Google Scholar 

  20. Saadatseresht M, Mansourian A, Taleai M (2009) Evacuation planning using multiobjective evolutionary optimization approach. Eur J Oper Res 198(1):305–314. https://doi.org/10.1016/j.ejor.2008.07.032

    Article  MATH  Google Scholar 

  21. Chu ML, Parigi P, Latombe JC, Law KH (2015) Simulating effects of signage, groups, and crowds on emergent evacuation patterns. AI Soc 30(4):493–507

    Article  Google Scholar 

  22. Yenumula K, Kolmer C, Pan J, Su X (2015) BIM-Controlled Signage System for Building Evacuation. Procedia Eng 118:284–289. https://doi.org/10.1016/j.proeng.2015.08.428

    Article  Google Scholar 

  23. Cristiani E, Peri D (2017) Handling obstacles in pedestrian simulations: Models and optimization. Appl Math Model 45:285–302. https://doi.org/10.1016/j.apm.2016.12.020

    Article  MathSciNet  MATH  Google Scholar 

  24. Eng Aik L, Choon TW (2013) Microscopic dynamics of pedestrian evacuation in hypermarket. Int J Eng 4(5)

  25. Zhao Y et al (2017) Optimal layout design of obstacles for panic evacuation using differential evolution. Phys A Stat Mech its Appl 465:175–194. https://doi.org/10.1016/j.physa.2016.08.021

    Article  MathSciNet  MATH  Google Scholar 

  26. Shende A, Singh MP, Kachroo P (2011) Optimization-based feedback control for pedestrian evacuation from an exit corridor. IEEE Trans Intell Transp Syst 12(4):1167–1176. https://doi.org/10.1109/TITS.2011.2146251

    Article  Google Scholar 

  27. Kamkarian P, Hexmoor H (2014) Exploiting the Imperialist Competition Algorithm to determine exit door efficacy for public buildings. SIMULATION 90(1):24–51

    Article  Google Scholar 

  28. Huan-Huan T, Li-Yun D, Yu X (2015) Influence of the exits’ configuration on evacuation process in a room without obstacle. Phys A Stat Mech Appl 420:164–178. https://doi.org/10.1016/j.physa.2014.10.002

    Article  Google Scholar 

  29. Wu J, Wang X, Chen J, Shu G, Li Y (2015) The position of a door can significantly impact on pedestrians’ evacuation time in an emergency. Appl Math Comput 258:29–35

    MATH  Google Scholar 

  30. Kurdi HA, Al-Megren S, Althunyan R, Almulifi A (2018) Effect of exit placement on evacuation plans. Eur J Oper Res. https://doi.org/10.1016/j.ejor.2018.01.050

    Article  MATH  Google Scholar 

  31. Gao D, Wang G-G, Pedrycz W (2020) Solving fuzzy job-shop scheduling problem using DE algorithm improved by a selection mechanism. IEEE Trans Fuzzy Syst 28(12):3265–3275

    Article  Google Scholar 

  32. Akay B, Karaboga D (2009) Parameter tuning for the artificial bee colony algorithm. https://doi.org/10.1007/978-3-642-04441-0-53

  33. Rani MR, Selamat H, Zamzuri H, Ahmad F (2011) PID controller optimization for a rotational inverted pendulum using genetic algorithm. https://doi.org/10.1109/ICMSAO.2011.5775461

  34. Ismail FS, Yusof R, Khalid M, Ibrahim Z, Selamat H (2012) Performance evaluation of self organizing genetic algorithm for multi-objective optimization problems. ICIC Express Lett 6(1):1–7

    Article  Google Scholar 

  35. Khamis N, Selamat H, Ismail FS, Lutfy OF (2019) Optimal exit configuration of factory layout for a safer emergency evacuation using crowd simulation model and multi-objective artificial bee colony optimization. Int J Integr Eng. https://doi.org/10.30880/ijie.2019.11.04.020

    Article  MATH  Google Scholar 

Download references

Acknowledgements

The authors would like to thank Universiti Teknologi Malaysia and the Ministry of Higher Education for their supports. This project was supported by Research University Grant Vote 00L31.

Author information

Authors and Affiliations

  1. Department of Data Science, Universiti Malaysia Kelantan, City Campus, Pengkalan Chepa, 16100, Kota Bharu, Kelantan, Malaysia

    Nurulaqilla Khamis

  2. Centre for Artificial Intelligence and Robotics, School of Electrical Engineering, Faculty of Engineering, Universiti Teknologi Malaysia, 81310, Johor Bahru Johor, Malaysia

    Hazlina Selamat

  3. Control & Mechatronic Engineering Department, School of Electrical Engineering, Faculty of Engineering, Universiti Teknologi Malaysia, 81310, Johor Bahru Johor, Malaysia

    Fatimah Sham Ismail

Authors
  1. Nurulaqilla Khamis
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Hazlina Selamat
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Fatimah Sham Ismail
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Nurulaqilla Khamis.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Khamis, N., Selamat, H. & Ismail, F.S. Improved optimization parameters prediction using the modified mega trend diffusion function for a small dataset problem. Knowl Inf Syst 64, 3129–3149 (2022). https://doi.org/10.1007/s10115-022-01727-z

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10115-022-01727-z

Keywords