Skip to main content
SpringerLink
Log in

Research of assembling optimized classification algorithm by neural network based on Ordinary Least Squares (OLS)

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

Abstract

A new optimized classification algorithm assembled by neural network based on Ordinary Least Squares (OLS) is established here. While recognizing complex high-dimensional data by neural network, the design of network is a challenge. Besides, single network model can hardly get satisfying recognition accuracy. Firstly, feature dimension reduction is carried on so that the design of network is more convenient. Take Elman neural network algorithm based on PCA as sub-classifier I. The recognition precision of this classifier is relatively high, but the convergence rate is not satisfying. Take RBF neural network algorithm based on factor analysis as sub-classifier II. The convergence rate of the classifier algorithm is fast, but the recognition precision is relatively low. In order to make up for the deficiency, by carrying on ensemble learning of the two sub-classifiers and determining optimal weights of each sub-classifier by OLS principle, assembled optimized classification algorithm is obtained, so to some extent, information loss caused by dimensionality reduction in data is made up. In the end, validation of the model can be tested by case analysis.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  1. Mccllochw S, Pitts W (1943) A logical calculus of the ideas immanent in nervous activity. Bull Math Biol 10(5):115–133

    Google Scholar 

  2. Ding SF, Jia WK, Su CY et al (2011) Research of neural network algorithm based on factor analysis and cluster analysis. Neural Comput Appl 20(2):297–302

    Article  Google Scholar 

  3. Sun JX (2002) Modern pattern recognition. National University of Defence Technology Press, Changsha

    Google Scholar 

  4. Bian ZQ, Zhang XG (2000) Pattern recognition. Tsinghua University Press, Beijing

    Google Scholar 

  5. Moody J, Dkaren CJ (1989) Fast learning in networks locally-tuned processing units. Neural Comput 1(2):281–294

    Article  Google Scholar 

  6. Elman JL (1990) Finding structure in time. Cogn Sci 14(2):179–211

    Article  Google Scholar 

  7. Tang CS, Jin YH (2003) A multiple classifiers integration method based on full information matrix. J Softw 14(6):1103–1109

    MATH  Google Scholar 

  8. Sun L, Han CZ, Shen JJ et al (2008) Generalized rough set method for ensemble feature selection and multiple classifier fusion. Acta Automatica Sinica 34(3):298–304

    Article  MATH  Google Scholar 

  9. Gu Y, Xu ZB, Sun J et al (2006) An intrusion detection ensemble system based on the features extracted by PCA and ICA. J Comput Res Develop 43(4):633–638

    Article  Google Scholar 

  10. Ding SF, Jia WK, Su CY et al (2008) Research of pattern feature extraction and selection. Proc seventh Int Conf Mach Learn Cybernetics 1:466–471

    Google Scholar 

  11. Ding SF, Jia WK, Su CY et al (2008) A survey on statistical pattern feature extraction. Lect Notes Artif Intell 5227:701–708

    Google Scholar 

  12. Foman G (2003) An exnetsive empirical study of feater selection metrics for text classification. J Mach Learn Res 3:1289–1305

    Google Scholar 

  13. Johnson RA, Wichern DW (2007) Applied multivariate statistical analysis, 6th edn. Prentice Hall, Englewood Cliffs

  14. Rumelhart DE, Hinton GE, Williams RJ (1986) Learning representation by back-propagating errors. Nature 3(6):533–536

    Article  Google Scholar 

  15. Ding SF, Jia WK, Su CY et al (2008) PCA-based Elman neural network algorithm. Lect Notes Comput Sci 5370:315–321

    Article  Google Scholar 

  16. Zhou ZH, Chen SF (2002) Neural network ensemble. Chinese J Comput 25(1):1–8

    MathSciNet  Google Scholar 

  17. Liu Y, Yao X (1999) Ensemble learning via negative correlation. Neural Netw 12(10):1399–1404

    Article  Google Scholar 

  18. http://archive.ics.uci.edu/ml/machine-learning-databases/parkinsons/[OL].2009.3

Download references

Acknowledgments

This work is supported by the Basic Research Program (Natural Science Foundation) of Jiangsu Province of China (No.BK2009093), the National Natural Science Foundation of China (No.60975039, and No.41074003), and the Opening Foundation of Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (No.IIP2010-1).

Author information

Authors and Affiliations

  1. School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, 221116, People’s Republic of China

    Xinzheng Xu, Shifei Ding, Weikuan Jia & Gang Ma

  2. Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, 100080, China

    Shifei Ding

  3. Geomatics College, Shandong University of Science and Technology, Qingdao, 266510, China

    Fengxiang Jin

Authors
  1. Xinzheng Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shifei Ding
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Weikuan Jia
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Gang Ma
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Fengxiang Jin
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Shifei Ding.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Xu, X., Ding, S., Jia, W. et al. Research of assembling optimized classification algorithm by neural network based on Ordinary Least Squares (OLS). Neural Comput & Applic 22, 187–193 (2013). https://doi.org/10.1007/s00521-011-0694-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-011-0694-3

Keywords

Search

Navigation