[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ Skip to main content
Springer Nature Link
Log in

Feature subset selection combining maximal information entropy and maximal information coefficient

  • Published:
Applied Intelligence Aims and scope Submit manuscript

Abstract

Feature subset selection is an efficient step to reduce the dimension of data, which remains an active research field in decades. In order to develop highly accurate and fast searching feature subset selection algorithms, a filter feature subset selection method combining maximal information entropy (MIE) and the maximal information coefficient (MIC) is proposed in this paper. First, a new metric mMIE-mMIC is defined to minimize the MIE among features while maximizing the MIC between the features and the class label. The mMIE-mMIC algorithm is designed to evaluate whether a candidate subset is valid for classification. Second, two searching strategies are adopted to identify a suitable solution in the candidate subset space, including the binary particle swarm optimization algorithm (BPSO) and sequential forward selection (SFS). Finally, classification is performed on UCI datasets to validate the performance of our work compared to 9 existing methods. Experimental results show that in most cases, the proposed method behaves equally or better than the other 9 methods in terms of classification accuracy and F1-score.

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
£29.99 /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 includes VAT (United Kingdom)

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

Similar content being viewed by others

Explore related subjects

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

References

  1. Lei L, Hao Q, Zhong Z (2016) Mode Selection and Resource Allocation in Device-to-Device Communications With User Arrivals and Departures. IEEE Access 4:5209–5222

    Article  Google Scholar 

  2. Dougherty ER (2001) Small sample issues for microarray-based classification. Comp Funct Genomics 2(1):28–34

    Article  Google Scholar 

  3. Xue Y, Zhang L, Wang B et al (2018) Nonlinear feature selection using Gaussian kernel SVM-RFE for fault diagnosis. Appl Intell 48(10):3306–3331

    Article  Google Scholar 

  4. Fodor IK (2002) A survey of dimension reduction techniques. Neoplasia 7(5):475–485

    Google Scholar 

  5. Aldehim G, Wang W (2017) Determining appropriate approaches for using data in feature selection[J]. Int J Mach Learn Cybern 8(3):915–928

    Article  Google Scholar 

  6. Liu H, Yu L (2005) Toward integrating feature selection algorithms for classification and clustering. IEEE Trans Knowl Data Eng 17(4):491–502

    Article  Google Scholar 

  7. Liu Y, Tang F, Zeng Z (2015) Feature selection based on dependency margin. IEEE Transactions on Cybernetics 45(6):1209–1221

    Article  Google Scholar 

  8. Hadi Z, Niazi M (2016) Relevant based structure learning for feature selection. Eng Appl Artif Intell 55:93–102

    Article  Google Scholar 

  9. Zhou Z (2016) Machine Learning. Tsinghua Press

  10. Kira K, Rendell LA (1992) A practical approach to feature selection, International Workshop on Machine Learning Morgan Kaufmann Publishers Inc. 249-256

  11. Cai Z, Gu J, Chen H (2017) A new hybrid intelligent framework for predicting Parkinson’s disease. IEEE Access, PP(99):1-1

  12. Song E et al. (2011) A feature selection approach to estimate discrimination capability of feature sub-set category, Journal of Huazhong University of Science & Technology

  13. Cai H, Ng M (2012) Feature weighting by RELIEF based on local hyperplane approximation, Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining Springer-Verlag, 335-346

  14. Baskar SS, Arockiam L (2014) C-LAS Relief-An improved feature selection technique in data mining. Int J Comput Appl 83(13):33–36

    Google Scholar 

  15. Khosravi MH , Bagherzadeh P (2018) A new method for feature selection based on intelligent water drops[J]. Applied Intelligence

  16. Qiao LY, Peng XY, Peng Y (2006) BPSO-SVM wrapper for feature subset selection. Acta Electron Sin 34(3):496–498

    Google Scholar 

  17. Wang Y, Feng L, Zhu J (2017) Novel artificial bee colony based feature selection method for filtering redundant information[J]. Appl Intell 48(3):868–885

    Google Scholar 

  18. Ran GB, Navot A, Tishby N (2004) Margin based feature selection - theory and algorithms, International Conference on Machine Learning ACM,43

  19. Hedjazi L et al (2015) Membership-margin based feature selection for mixed type and high-dimensional data: Theory and applications. Inf Sci 322:174–196

    Article  MathSciNet  Google Scholar 

  20. Wei P et al (2014) Comparative analysis on margin based feature selection algorithms. Int J Mach Learn Cybern 5(3):339–367

    Article  Google Scholar 

  21. Ding C, Peng H, (2003) Minimum redundancy feature selection from microarray gene expression data, Bio-informatics Conference, 2003. Csb 2003. Proceedings of the. IEEE, 523-528

  22. Peng H, Long F, Ding C Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans Pattern Anal Mach Intell 27(8):1226–1238

  23. Liu C, Wang W, Zhao Q et al (2017) A new feature selection method based on a validity index of feature subset[J]. Pattern Recogn Lett 92:1–8

    Article  Google Scholar 

  24. Che J et al. (2017) Maximum relevance minimum common redundancy feature selection for nonlinear data, Information Sciences 409

  25. Roffo G , Melzi S , Castellani U , et al. (2017) [IEEE 2017 IEEE International Conference on Computer Vision (ICCV) - Venice (2017.10.22-2017.10.29)] 2017 IEEE International Conference on Computer Vision (ICCV) - Infinite Latent Feature Selection: A Probabilistic Latent Graph-Based Ranking Approach[J]. 1407-1415

  26. Zheng K, Wang X (2018) Feature selection method with joint maximal information entropy between features and class. Pattern Recogn 77:20–29

    Article  Google Scholar 

  27. Xu Y, Ding YX, Ding J et al (2016) Mal-Lys: prediction of lysine malonylation sites in proteins integrated sequence-based features with mRMR feature selection. Sci Rep 6:38318

    Article  Google Scholar 

  28. Vinh LT, Lee S, Park YT et al (2012) A novel feature selection method based on normalized mutual information[J]. Appl Intell 37(1):100–120

    Article  Google Scholar 

  29. Zhao G, Liu S (2016) Estimation of discriminative feature subset using community modularity. Sci Rep 6:25040

    Article  Google Scholar 

  30. Geiβ S, Einax J (1996) Multivariate correlation analysis - a method for the analysis of multidimensional time series in environmental studies. Chemom Intell Lab Syst 32(1):57–65

    Article  Google Scholar 

  31. Hall MA (2000) Correlation-based feature selection for discrete and numeric class machine learning, Proceedings of the Seventeenth International Conference on Machine Learning Morgan Kaufmann Publishers Inc. 359-366

  32. Reshef DN, Reshef YA, Finucane HK et al (2011) Detecting novel associations in large datasets. Science 334:1518–1524

    Article  Google Scholar 

  33. Ya-hong Z, Li Y-j, Ting Z (2015) Detecting multivariable correlation with maximal information entropy. J Electron Inf Technol 37(1):123–129

    Google Scholar 

  34. Reshef D, Reshef Y, Sabeti P, Mitzenmacher M, MINE: Maximal Infor-mation-based Nonparametric Exploration. [Online], available at: http://www.exploredata.net/

  35. Unler A, Murat A, Chinnam RB (2011) mr 2 PSO: A maximum relevance minimum redundancy feature selection method based on swarm intelligence for support vector machine classification. Inf Sci 181(20):4625–4641

    Article  Google Scholar 

  36. Kennedy J, Eberhart R (1997) A discrete binary version of the particle swarm algorithm, IEEE International Conference on Systems, Man, and Cybernetics. IEEE Comput Cybern Simul 2002(5):4104–4108

    Google Scholar 

  37. UCI Machine Learning Repository. http://archive.ics.uci.edu/ml/

  38. Chang CC, Lin CJ (2011) LIBSVM: A library for support vector machines. ACM Trans Intell Syst Technol 2(3):389–396 Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm

    Article  Google Scholar 

  39. Tibshirani RJ (1996) Regression Shrinkage and Selection via the LASSO[J]. J R Stat Soc Ser B Methodol 73(1):273–282

    MathSciNet  MATH  Google Scholar 

  40. He X, Cai D, Niyogi P (2005) Laplacian score for feature selection. In NIPS

  41. Fisher: P. E. H. R. O. Duda and D. G. Stork. Pattern Classification. Wiley-Interscience Publication, 2001

  42. Roffo G, Melzi S, Cristani M (2015) Infinite Feature Selection[C]// IEEE International Conference on Computer Vision

  43. IRA Hamid JA, Kim TH (2013) Using feature selection and classification scheme for automating phishing email detection. Stud Inform Control 22(1):61–70

    Google Scholar 

  44. Toolan F, Carthy J (2009) Phishing detection using classifier ensembles, Ecrime Researchers Summit, IEEE Xplore, 1-9

  45. Zhang Y et al (2014) A novel algorithm for the precise calculation of the maximal information coefficient. Sci Rep 4(4):6662

    Google Scholar 

  46. Kinney JB, Atwal GS (2014) Equitability, mutual information, and the maximal information coefficient[J]. Proc Natl Acad Sci U S A 111(9):3354

    Article  MathSciNet  Google Scholar 

Download references

Acknowledgments

Xiujuan Wang is the corresponding author. This work was supported by the National Key R&D Program of China [NO. 2017YFB0802703] and the National Natural Science Foundation of China [NO.61602052].

Author information

Authors and Affiliations

  1. School of Cyberspace Security, Beijing University of Posts and Telecommunications, Beijing, China

    Kangfeng Zheng, Bin Wu & Tong Wu

  2. Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China

    Xiujuan Wang

Authors
  1. Kangfeng Zheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xiujuan Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Bin Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Tong Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xiujuan Wang.

Additional information

Publisher’s note

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zheng, K., Wang, X., Wu, B. et al. Feature subset selection combining maximal information entropy and maximal information coefficient. Appl Intell 50, 487–501 (2020). https://doi.org/10.1007/s10489-019-01537-x

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10489-019-01537-x

Keywords

Search

Navigation