Skip to main content
Springer Nature Link
Log in

Multi-output incremental back-propagation

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

Deep learning techniques can form generalized models that can solve any problem that is not solvable by traditional approaches. It explains the omnipresence of deep learning models across all domains. However, a lot of time is spent on finding the optimal hyperparameters to help the model generalize and give the highest accuracy. This paper investigates a proposed model incorporating hybrid layers and a novel approach for weight initialization aimed at—(1) Reducing the overall trial and error time spent in finding the optimal number of layers by providing the necessary insights. (2) Reducing the randomness in weight initialization with the help of a novel incremental backpropagation based model architecture. The model, along with the principal component analysis-based initialization, substantially provides a stable weight initialization, thereby improving the train and test performance and speeding up the process of convergence to an optimal solution. Furthermore, three data sets were tested on the proposed approach, and they outperformed the state-of-the-art initialization methods.

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

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

Data availability

The data sets generated during and/or analyzed during the current study are available in the open-source Kaggle repository. The following are details. 1. Fashion MNIST Data Set: https://www.kaggle.com/datasets/zalando-research/fashionmnist. 2. Intel Image Classification Data set: https://www.kaggle.com/datasets/puneet6060/intel-image-classification. 3. EMNIST Letters Data Set: https://www.kaggle.com/datasets/crawford/emnist.

Notes

  1. https://colab.research.google.com/.

References

  1. Baldominos A, Saez Y, Isasi P (2020) On the automated, evolutionary design of neural networks: past, present, and future. Neural Comput Appl 32(2):519–545

    Article  Google Scholar 

  2. Basodi S, Ji C, Zhang H, Pan Y (2020) Gradient amplification: an efficient way to train deep neural networks. Big Data Min Anal 3(3):196–207

    Article  Google Scholar 

  3. Cohen G, Afshar S, Tapson J, van Schaik A (2017) Emnist: an extension of mnist to handwritten letters. CoRR abs/1702.05373

  4. Ding S, Li H, Su C, Yu J, Jin F (2013) Evolutionary artificial neural networks: a review. Artif Intell Rev 39(3):251–260

    Article  Google Scholar 

  5. Fahlman SE, Lebiere C (1990) The cascade-correlation learning architecture. CARNEGIE-MELLON, Pittsburgh

    Google Scholar 

  6. Frean M (1990) The upstart algorithm: a method for constructing and training feedforward neural networks. Neural Comput 2(2):198–209

    Article  Google Scholar 

  7. Gu J, Wang Z, Kuen J, Ma L, Shahroudy A, Shuai B, Liu T, Wang X, Wang G, Cai J, Chen T (2018) Recent advances in convolutional neural networks. Pattern Recognit 77:354–377

    Article  Google Scholar 

  8. Han Y, Huang G, Song S, Yang L, Wang H, Wang Y (2022) Dynamic neural networks: a survey. IEEE Trans Pattern Anal Mach Intell 44(11):7436–7456

    Article  Google Scholar 

  9. Hancock JT, Khoshgoftaar TM (2020) Survey on categorical data for neural networks. J Big Data 7(1):1–41

    Article  Google Scholar 

  10. He H, Chen M, Xu G, Zhu Z, Zhu Z (2021) Learnability and robustness of shallow neural networks learned by a performance-driven bp and a variant of pso for edge decision-making. Neural Comput Appl 33:13809–13830

    Article  Google Scholar 

  11. Hu YQ, Yu Y (2020) A technical view on neural architecture search. Int J Mach Learn Cybern 11(4):795–811

    Article  MathSciNet  Google Scholar 

  12. Huang G, Sun Y, Liu Z, Sedra D, Weinberger K (2016) Deep networks with stochastic depth. In: Computer Vision—European conference on computer vision (ECCV) 2016, vol 9908, pp 646–661

  13. Igomu E, Ige E, Adesina O (2022) Coupled modeling and process optimization in a genetic-algorithm paradigm for reverse osmosis dialysate production plant. S Afr J Chem Eng 42:337–350

    Google Scholar 

  14. Kaggle (2019) Intel image classification dataset. https://www.kaggle.com/puneet6060/intel-image-classification

  15. Kavzoglu T (1999) Determining optimum structure for artificial neural networks. In: Annual Technical Conference and Exhibition of the Remote Sensing Society

  16. Li J, Han P, Ren X, Hu J, Chen L, Shang S (2021) Sequence labeling with meta-learning. IEEE Trans Knowl Data Eng. https://doi.org/10.1109/TKDE.2021.3118469

    Article  Google Scholar 

  17. Li J, Shang S, Chen L (2021) Domain generalization for named entity boundary detection via metalearning. IEEE Trans Neural Netw Learn Syst 32(9):3819–3830. https://doi.org/10.1109/TNNLS.2020.3015912

    Article  Google Scholar 

  18. Li J, Sun A, Ma Y (2021) Neural named entity boundary detection. IEEE Trans Knowl Data Eng 33(4):1790–1795. https://doi.org/10.1109/TKDE.2020.2981329

    Article  Google Scholar 

  19. Li Z, Liu F, Yang W, Peng S, Zhou J (2022) A survey of convolutional neural networks: Analysis, applications, and prospects. IEEE Trans Neural Netw Learn Syst 33(12):6999–7019

    Article  MathSciNet  Google Scholar 

  20. Liu Y, Zhou C, Chen Y (2006) Weight initialization of feedforward neural networks by means of partial least squares. In: 2006 International Conference on Machine Learning and Cybernetics, pp 3119–3122

  21. MacDonald G, Godbout A, Gillcash B, Cairns S (2019) Volume-preserving neural networks: A solution to the vanishing gradient problem. arXiv preprint arXiv:1911.09576

  22. Mansouri E, Manfredi M, Hu JW (2022) Environmentally friendly concrete compressive strength prediction using hybrid machine learning. Sustainability 14(20)

  23. Ojha VK, Abraham A, Snášel V (2017) Metaheuristic design of feedforward neural networks: a review of two decades of research. Eng Appl Artif Intell 60:97–116

    Article  Google Scholar 

  24. Owens F, Zheng F, Irvine D (2005) A multi-output-layer perceptron. Neural Comput Appl 4:10–20

    Article  Google Scholar 

  25. Seuret M, Alberti M, Liwicki M, Ingold R (2017) Pca-initialized deep neural networks applied to document image analysis. In: 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR), vol 01, pp 877–882

  26. Singh J, Banerjee R (2019) A study on single and multi-layer perceptron neural network. In: 2019 3rd International Conference on Computing Methodologies and Communication (ICCMC), pp 35–40

  27. Singh P, Varshney M, Namboodiri VP (2020) Cooperative initialization based deep neural network training. In: 2020 IEEE Winter Conference on Applications of Computer Vision (WACV), pp 1130–1139

  28. Sodhi SS, Chandra P, Tanwar S (2014) A new weight initialization method for sigmoidal feedforward artificial neural networks. In: 2014 International Joint Conference on Neural Networks (IJCNN), pp 291–298

  29. Sousa C (2016) An overview on weight initialization methods for feedforward neural networks. In: 2016 International Joint Conference on Neural Networks (IJCNN)

  30. Xiao H, Rasul K, Vollgraf R (2017) Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms. CoRR abs/1708.07747

  31. Yao X (1999) Evolving artificial neural networks. Proc IEEE 87(9):1423–1447

    Article  Google Scholar 

  32. Zaki MJ, Meira W Jr, Meira W (2014) Data mining and analysis: fundamental concepts and algorithms. Cambridge University Press, pp 548–583

    MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Engineering and Information Technology, Veermata Jijabai Technological Institute (VJTI), Mumbai, Maharashtra, 400019, India

    Rachana Chaudhari, Dhwani Agarwal, Kritika Ravishankar, Nikita Masand, Vijay K. Sambhe & Sandeep S. Udmale

Authors
  1. Rachana Chaudhari
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Dhwani Agarwal
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Kritika Ravishankar
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Nikita Masand
    View author publications

    You can also search for this author inPubMed Google Scholar

  5. Vijay K. Sambhe
    View author publications

    You can also search for this author inPubMed Google Scholar

  6. Sandeep S. Udmale
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Sandeep S. Udmale.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

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 (e.g. a society or other partner) 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

Chaudhari, R., Agarwal, D., Ravishankar, K. et al. Multi-output incremental back-propagation. Neural Comput & Applic 35, 14897–14910 (2023). https://doi.org/10.1007/s00521-023-08490-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-023-08490-4

Keywords