Movatterモバイル変換

[0]ホーム
URL:

Skip to main content

Advertisement

Springer Nature Link
Log in

Improved grey wolf optimization-based feature subset selection with fuzzy neural classifier for financial crisis prediction

  • Focus
  • Published:
Soft Computing Aims and scope Submit manuscript

Abstract

In present days, prediction of financial crisis of a company is a hot research area. The use of data mining and machine learning algorithms assists to resolve the financial crisis prediction (FCP) problem. Since financial data contain more demographical and unwanted information, it might decrease the classification performance significantly. So, feature selection (FS) process is applied to choose useful data and remove the irrelevant repetitive data. This paper introduces a novel predictive framework for FCP model by the incorporation of improved grey wolf optimization (IGWO) and fuzzy neural classifier (FNC). An IGWO algorithm is derived by the integration of GWO algorithm and tumbling effect. The presented IGWO-based FS method is employed to discover the optimal features from the financial data. For classification purposes, FNC is employed. The proposed method is experimented on two benchmark data sets, namely Australian Credit and German data set under several of performance metrics. The experimental values verified the superior nature of the proposed FCP model over the compared 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
¥17,985 /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 includes VAT (Japan)

Instant access to the full article PDF.

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.

References

  • El Aziz MA, Hassanien AE (2018) Modified cuckoo search algorithm with rough sets for feature selection. Neural Comput Appl 29(4):925–934

    Article  Google Scholar 

  • Geng R, Bose I, Chen X (2015) Prediction of financial distress: an empirical study of listed Chinese companies using data mining. Eur J Oper Res 241(1):236–247

    Article  Google Scholar 

  • Green TJ (2019) Big data analysis in financial markets. Doctoral dissertation, Colorado Technical University

  • Guyon I, Elisseeff A (2003) An introduction to variable and feature selection. J Mach Learn Res 3:1157–1182

    MATH  Google Scholar 

  • Hastie T, Tibshirani R, Friedman J, Franklin J (2005) The elements of statistical learning: data mining, inference and prediction. Math Intell 27(2):83–85

    Google Scholar 

  • Hofmann H (1996) German credit data.https://archive.ics.uci.edu/ml/datasets/statlog+(german+credit+data). Accessed 2 Aug 2018

  • Huang Z, Chen H, Hsu CJ, Chen WH, Wu S (2004) Credit rating analysis with support vector machines and neural networks: a market comparative study. Decis Support Syst 37(4):543–558

    Article  Google Scholar 

  • Kumar PR, Ravi V (2007) Bankruptcy prediction in banks and firms via statistical and intelligent techniques—a review. Eur J Oper Res 180(1):1–28

    Article  Google Scholar 

  • Kumar PM, Lokesh S, Varatharajan R, Babu GC, Parthasarathy P (2018) Cloud and IoT based disease prediction and diagnosis system for healthcare using Fuzzy neural classifier. Future Gener Comput Syst 86:527–534

    Article  Google Scholar 

  • Lin Y, Guo H, Hu J (2013) An SVM-based approach for stock market trend prediction. In: The 2013 international joint conference on neural networks (IJCNN). IEEE, pp 1–7

  • Lin F, Liang D, Yeh CC, Huang JC (2014) Novel feature selection methods to financial distress prediction. Expert Syst Appl 41(5):2472–2483

    Article  Google Scholar 

  • Mirjalili S, Mirjalili SM, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69:46–61

    Article  Google Scholar 

  • Muller SD, Marchetto J, Airaghi S, Kournoutsakos P (2002) Optimization based on bacterial chemotaxis. IEEE Trans Evol Comput 6(1):16–29

    Article  Google Scholar 

  • Quinlan (1987) Australian credit dataset. UCI Machine Learning Repository.http://archive.ics.uci.edu/ml/datasets/statlog+(australian+credit+approval). Accessed 2 Aug 2018

  • Rodríguez L, Castillo O, Soria J (2016) Grey wolf optimizer with dynamic adaptation of parameters using fuzzy logic. In: 2016 IEEE congress on evolutionary computation (CEC). IEEE, pp 3116–3123

  • Rodríguez L, Castillo O, Soria J, Melin P, Valdez F, Gonzalez CI, Martinez GE, Soto J (2017) A fuzzy hierarchical operator in the grey wolf optimizer algorithm. Appl Soft Comput 57:315–328

    Article  Google Scholar 

  • Sánchez D, Melin P, Castillo O (2017a) A grey wolf optimizer for modular granular neural networks for human recognition. In: Computational intelligence and neuroscience, 2017

  • Sánchez D, Melin P, Castillo O (2017b) Optimization of modular granular neural networks using a firefly algorithm for human recognition. Eng Appl Artif Intell 64:172–186

    Article  Google Scholar 

  • Sánchez D, Melin P, Castillo O (2017c) A grey wolf optimizer for modular granular neural networks for human recognition. In: Computational intelligence and neuroscience, 2017

  • Shankar K, Eswaran P (2015) A secure visual secret share (VSS) creation scheme in visual cryptography using elliptic curve cryptography with optimization technique. Aust J Basic Appl Sci 9(36):150–163

    Google Scholar 

  • Uthayakumar J, Vengattaraman T, Dhavachelvan P (2017) Swarm intelligence based classification rule induction (CRI) framework for qualitative and quantitative approach: an application of bankruptcy prediction and credit risk analysis. J King Saud Univ Comput Inf Sci

  • Uthayakumar J, Metawa N, Shankar K, Lakshmanaprabu SK (2018a) Financial crisis prediction model using ant colony optimization. Int J Inf Manag

  • Uthayakumar J, Metawa N, Shankar K, Lakshmanaprabu SK (2018b) Financial crisis prediction model using ant colony optimization. Int J Inf Manag.https://doi.org/10.1016/j.ijinfomgt.2018.12.001

    Article  Google Scholar 

  • Wang G, Ma J, Yang S (2014) An improved boosting based on feature selection for corporate bankruptcy prediction. Expert Syst Appl 41(5):2353–2361

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Information Technology, Babasaheb Bhimrao Ambedkar University, Lucknow, India

    Shweta Sankhwar

  2. Maharaja Agrasen Institute of Technology, Delhi, India

    Deepak Gupta

  3. Department of Electrical and Electronics Engineering, Sri Krishna College of Engineering and Technology, Coimbatore, India

    K. C. Ramya & S. Sheeba Rani

  4. Department of Computer Applications, Alagappa University, Karaikudi, Tamil Nadu, India

    K. Shankar

  5. Department of Electronics and Instrumentation Engineering, B. S. Abdur Rahman Crescent Institute of Science and Technology, Chennai, India

    S. K. Lakshmanaprabu

Authors
  1. Shweta Sankhwar

    You can also search for this author inPubMed Google Scholar

  2. Deepak Gupta

    You can also search for this author inPubMed Google Scholar

  3. K. C. Ramya

    You can also search for this author inPubMed Google Scholar

  4. S. Sheeba Rani

    You can also search for this author inPubMed Google Scholar

  5. K. Shankar

    You can also search for this author inPubMed Google Scholar

  6. S. K. Lakshmanaprabu

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence toS. K. Lakshmanaprabu.

Ethics declarations

Conflict of interest

The authors has expressed no conflict of interest.

Additional information

Communicated by O. Castillo, D. K. Jana.

Publisher's Note

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

Rights and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sankhwar, S., Gupta, D., Ramya, K.C.et al. Improved grey wolf optimization-based feature subset selection with fuzzy neural classifier for financial crisis prediction.Soft Comput24, 101–110 (2020). https://doi.org/10.1007/s00500-019-04323-6

Download citation

Keywords

Access this article

Subscribe and save

Springer+ Basic
¥17,985 /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 includes VAT (Japan)

Instant access to the full article PDF.

Advertisement

[8]ページ先頭
©2009-2025 Movatter.jp