SpringerLink
Log in

Graph-Based Supervised Feature Selection Using Correlation Exponential

  • Conference paper
  • First Online:
Emerging Technology in Modelling and Graphics

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 937))

Abstract

In this article, a graph-theoretic method for supervised feature selection using matrix exponential of pairwise correlation value has been illustrated. In machine learning, high-dimensional data sets have a enormous number of redundant and irrelevant features. The sum of mean and standard deviation of exponential matrix has been set as the threshold for selecting relevant features. Principles of vertex cover and independent set have then been used to remove redundant features. In the next step, mutual information value has been used to select relevant features that were initially rejected. The results show that this method has performed better than the benchmark algorithms when experimented on multiple standard data sets.

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

Access this chapter

Log in via an institution
Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now
Chapter
EUR 29.95
Price includes VAT (Germany)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
EUR 160.49
Price includes VAT (Germany)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
EUR 213.99
Price includes VAT (Germany)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free ship** worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. E.R. Dougherty, J. Hua, W. Tembe, Performance of feature-selection methods in the classification of high-dimension data. Pattern Recogn. 42, 409–424 (2009)

    Article  Google Scholar 

  2. M. Dash, H. Liu, Feature selection for classification. Intell. Data Anal. 1, 131156 (1997)

    Article  Google Scholar 

  3. G.H. John, R. Kohavi, Wrappers for feature subset selection. Artif. Intell. 97, 273–324 (1997)

    Article  Google Scholar 

  4. P.N. Koch, T.W. Simpson, J.K. Allen, F. Mistree, Statistical approximations for multidisciplinary design optimization: the problem of size. J. Aircr. 36(1), 275286 (1999)

    Article  Google Scholar 

  5. G. Li, S.-W. Wang, C. Rosenthal, H. Rabitz, High dimensional model representations generated from low dimensional data samples. I. mp-Cut-HDMR. J. Math. Chem. 30(1), 130 (2001b)

    Article  MathSciNet  Google Scholar 

  6. G.H. John, R. Kohavi, K. Peger, Irrelevant features and the subset selection problem, in ICML (1994)

    Google Scholar 

  7. S.E. Schaeffer, Graph clustering. Comput. Sci. Rev. 1, 2764 (2007)

    Article  Google Scholar 

  8. Z. Zhang, E.R. Hancock, A graph-based approach to feature selection, in GBRPR (2011)

    Google Scholar 

  9. S. Bandyopadhyay, T. Bhadra, P. Mitra, U. Maulik, Integration of dense subgraph nding with feature clustering for unsupervised feature selection. Pattern Recogn. Lett. 40, 104112 (2014)

    Article  Google Scholar 

  10. Q. Song, J. Ni, G. Wang, A fast clustering-based feature subset selection algorithm for high-dimensional data. IEEE Trans. Knowl. Data Eng. 25, 114 (2013)

    Article  Google Scholar 

  11. P. Moradi, M. Rostami, A graph theoretic approach for unsupervised feature selection. Eng. Appl. Artif. Intell. 44, 3345 (2015a)

    Article  Google Scholar 

  12. A. Strehl, J. Ghosh, Cluster ensembles a knowledge reuse framework for combining multiple partitions. J. Mach. Learn. Res. 3, 583617 (2002)

    MathSciNet  MATH  Google Scholar 

  13. T.M. Cover, J.A. Thomas, Elements of Information Theory (Wiley, New York, USA, 2012)

    MATH  Google Scholar 

  14. M. Lichman, UCI machine learning repository (2013). http://archive.ics.uci.edu/ml

  15. M.A. Hall, Correlation-based feature selection for machine learning

    Google Scholar 

  16. H. Peng, F. Long, C.H. Ding, Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans. Pattern Anal. Mach. Intell. 27, 1226–1238 (2005)

    Article  Google Scholar 

  17. A.K. Das, S. Goswami, B. Chakraborty, A. Chakrabarti, A graph-theoretic approach for visualization of data set feature association, in ACSS (2016)

    Google Scholar 

  18. M. Dash, H. Liu, Feature selection for clustering, in PAKDD (2000)

    Google Scholar 

  19. N. Deo, Graph Theory with Applications to Engineering and Computer Science (PHI, 1979)

    Google Scholar 

  20. S. Goswami, A. Chakrabarti, B. Chakraborty, An efficient feature selection technique for clustering based on a new measure of feature importance. J. Intell. Fuzzy Syst. 112

    Google Scholar 

  21. S. Goswami, A.K. Das, A. Chakrabarti, B. Chakraborty, A feature cluster taxonomy based feature selection technique. Expert Syst. Appl. 79, 7689 (2017)

    Article  Google Scholar 

  22. P. Moradi, M. Rostami, Integration of graph clustering with ant colony optimization for feature selection. Knowl. Based Syst. 84, 144161 (2015b)

    Article  Google Scholar 

  23. Z. Zhang, E.R. Hancock, Hypergraph based information-theoretic feature selection. Pattern Recogn. Lett. 33, 19911999 (2012)

    Google Scholar 

  24. E. Estrada, J.A. Rodguez-Velzquez, Subgraph centrality in complex networks. Phys. Rev. E Stat. Nonlin. Soft Matter Phys. 71(5 Pt 2), 056103 (2005)

    Article  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institute of Engineering and Management, Kolkata, India

    Gulshan Kumar, Gitesh Jain & Amit Kumar Das

  2. University of Calcutta, Salt Lake, Kolkata, India

    Mrityunjoy Panday & Saptarsi Goswami

Authors
  1. Gulshan Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Gitesh Jain
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mrityunjoy Panday
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Amit Kumar Das
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Saptarsi Goswami
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Gulshan Kumar .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, University of Kalyani, Kalyani, West Bengal, India

    Jyotsna Kumar Mandal

  2. Department of Computer Science and Engineering, Institute of Engineering and Management, Kolkata, West Bengal, India

    Debika Bhattacharya

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Kumar, G., Jain, G., Panday, M., Das, A.K., Goswami, S. (2020). Graph-Based Supervised Feature Selection Using Correlation Exponential. In: Mandal, J., Bhattacharya, D. (eds) Emerging Technology in Modelling and Graphics. Advances in Intelligent Systems and Computing, vol 937. Springer, Singapore. https://doi.org/10.1007/978-981-13-7403-6_4

Download citation

  • DOI: https://doi.org/10.1007/978-981-13-7403-6_4

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-7402-9

  • Online ISBN: 978-981-13-7403-6

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation