Symmetric uncertainty class-feature association map for feature selection in microarray dataset

  • Soodeh Bakhshandeh
  • Reza AzmiEmail author
  • Mohammad Teshnehlab
Original Article


For a huge number of features versus a small size of samples, feature selection methods are useful preprocessing approaches that could eliminate the irrelevant and redundant features from the final feature subset. One of the recent research areas in feature selection is DNA microarray that the number of dimensions increase fast and requires further research in the field of feature selection. Modeling the feature search space as a graph leads to improving the visualizing of features and using graph theoretic concepts in the feature selection process. In this paper, a filer-based feature selection algorithm using graph technique is proposed for reducing the dimension of dataset named as Symmetric Uncertainty Class-Feature Association Map feature selection (SU-CFAM). In the first step, it uses the Symmetric Uncertainty concept for visualizing the feature search space as a graph. After clustering the graph into several clusters using a community detection algorithm, SU-CFAM constructs an adjacency matrix for each cluster and the final subset is selected by using the concept of maximal independent set. The performance of SU-CFAM has been compared with five well-known feature selection approaches using three classifiers including SVM, DT, NB. Experiments on fifteen public DNA microarray datasets show that SU-CFAM can achieve a better classification performance compared with other methods.


Feature selection Microarray data Graph-theoretic approach Class-Feature Association Map 



  1. 1.
    Hu X, Zhou P, Li P, Wang J, Wu X (2016) A survey on online feature selection with streaming features. Front Comput Sci 1–15Google Scholar
  2. 2.
    Das AK, Goswami S, Chakrabarti A, Chakraborty B (2017) A new hybrid feature selection approach using feature association map for supervised and unsupervised classification. Expert Syst Appl 88(supplement C):81–94Google Scholar
  3. 3.
    Chen T, Hong Z, Deng Fa, Yang X, Wei J, Cui M (2015) A novel selective ensemble classification of microarray data based on teaching-learning-based optimization. Int J Multimed Ubiquitous Eng 10(6):203–218Google Scholar
  4. 4.
    Hoque N, Bhattacharyya D, Kalita JK (2014) Mifs-nd: a mutual information-based feature selection method. Expert Syst Appl 41(14):6371–6385Google Scholar
  5. 5.
    Liao B, Jiang Y, Liang W, Zhu W, Cai L, Cao Z (2014) Gene selection using locality sensitive laplacian score. IEEE/ACM Trans Comput Biol Bioinform 11(6):1146–1156Google Scholar
  6. 6.
    Solorio-Fernandez S, Carrasco-Ochoa JA, Martínez-Trinidad JF (2016) A new hybrid filter-wrapper feature selection method for clustering based on ranking. Neurocomputing 214:866–880Google Scholar
  7. 7.
    Theodoridis S, Koutroumbas K (2008) Pattern recognition, 4th edn. Academic Press, OxfordzbMATHGoogle Scholar
  8. 8.
    Lai CM, Yeh WC, Chang CY (2016) Gene selection using information gain and improved simplified swarm optimization. Neurocomputing 218:331–338Google Scholar
  9. 9.
    Radovic M, Ghalwash M, Filipovic N, Obradovic Z (2017) Minimum redundancy maximum relevance feature selection approach for temporal gene expression data. BMC Bioinform 18(1):9Google Scholar
  10. 10.
    Peker M, Sen B, Delen D (2015) Computer-aided diagnosis of parkinson’s disease using complex-valued neural networks and mrmr feature selection algorithm. J Healthcare Eng 6(3):281–302Google Scholar
  11. 11.
    Sun S, Peng Q, Shakoor A (2014) A kernel-based multivariate feature selection method for microarray data classification. PloS one 9(7):e102541Google Scholar
  12. 12.
    Labani M, Moradi P, Ahmadizar F, Jalili M (2018) A novel multivariate filter method for feature selection in text classification problems. Eng Appl Artif Intell 70:25–37Google Scholar
  13. 13.
    Ferreira AJ, Figueiredo MA (2012) An unsupervised approach to feature discretization and selection. Pattern Recognit 45(9):3048–3060Google Scholar
  14. 14.
    Ferreira AJ, Figueiredo MA (2012) Efficient feature selection filters for high-dimensional data. Pattern Recognit Lett 33(13):1794–1804Google Scholar
  15. 15.
    Tabakhi S, Moradi P, Akhlaghian F (2014) An unsupervised feature selection algorithm based on ant colony optimization. Eng Appl Artif Intell 32(supplement C):112–123Google Scholar
  16. 16.
    Cheriguene S, Azizi N, Zemmal N, Dey N, Djellali H, Farah N (2016) Optimized tumor breast cancer classification using combining random subspace and static classifiers selection paradigms. Applications of intelligent optimization in biology and medicine. Springer, Cham, pp 289–307Google Scholar
  17. 17.
    Haindl M, Somol P, Ververidis D, Kotropoulos C (2006) Feature selection based on mutual correlation. Springer, Berlin Heidelberg, pp 569–577Google Scholar
  18. 18.
    Brusco MJ (2014) A comparison of simulated annealing algorithms for variable selection in principal component analysis and discriminant analysis. Computat Stat Data Anal 77:38–53MathSciNetzbMATHGoogle Scholar
  19. 19.
    Li Y, Wang G, Chen H, Shi L, Qin L (2013) An ant colony optimization based dimension reduction method for high-dimensional datasets. J Bionic Eng 10(2):231–241Google Scholar
  20. 20.
    Kabir MM, Shahjahan M, Murase K (2012) A new hybrid ant colony optimization algorithm for feature selection. Expert Syst Appl 39(3):3747–3763Google Scholar
  21. 21.
    Sahu B, Mishra D (2012) A novel feature selection algorithm using particle swarm optimization for cancer microarray data. Proc Eng 38(Supplement C):27–31Google Scholar
  22. 22.
    Martinez E, Alvarez MM, Trevino V (2010) Compact cancer biomarkers discovery using a swarm intelligence feature selection algorithm. Comput Biol Chem 34(4):244–250Google Scholar
  23. 23.
    Oreski S, Oreski G (2014) Genetic algorithm-based heuristic for feature selection in credit risk assessment. Expert Syst Appl 41(4):2052–2064Google Scholar
  24. 24.
    Goswami S, Saha S, Chakravorty S, Chakrabarti A, Chakraborty B (2015) A new evaluation measure for feature subset selection with genetic algorithm. Int J Intell Syst Appl 7(10):28Google Scholar
  25. 25.
    Xue B, Zhang M, Browne WN, Yao X (2016) A survey on evolutionary computation approaches to feature selection. IEEE Trans Evol Comput 20(4):606–626Google Scholar
  26. 26.
    Shah M, Marchand M, Corbeil J (2012) Feature selection with conjunctions of decision stumps and learning from microarray data. IEEE Trans Pattern Anal Mach Intell 34(1):174–186Google Scholar
  27. 27.
    Huang ML, Hung YH, Lee W, Li R, Jiang BR (2014) Svm-rfe based feature selection and taguchi parameters optimization for multiclass svm classifier. Sci World JGoogle Scholar
  28. 28.
    Wang S, Tang J, Liu H (2015) Embedded unsupervised feature selection. In: AAA, pp 470–476Google Scholar
  29. 29.
    Mundra PA, Rajapakse JC (2010) Svm-rfe with mrmr filter for gene selection. IEEE Trans NanoBiosci 9(1):31–37Google Scholar
  30. 30.
    Chuang LY, Yang CH, Wu KC, Yang CH (2011) A hybrid feature selection method for dna microarray data. Comput Biol Med 41(4):228–237Google Scholar
  31. 31.
    Ghosh R, Kumar P, Roy PP (2018) A dempster–shafer theory based classifier combination for online signature recognition and verification systems. Int J Mach Learn Cybern 1–16Google Scholar
  32. 32.
    Kumar P, Roy PP, Dogra DP (2018) Independent bayesian classifier combination based sign language recognition using facial expression. Inf Sci 428:30–48MathSciNetGoogle Scholar
  33. 33.
    Kumar P, Saini R, Roy PP, Pal U (2018) A lexicon-free approach for 3d handwriting recognition using classifier combination. Pattern Recognit Lett 103:1–7Google Scholar
  34. 34.
    Santosh K, Roy PP (2018) Arrow detection in biomedical images using sequential classifier. Int J Mach Learn Cybern 9(6):993–1006Google Scholar
  35. 35.
    Song Q, Ni J, Wang G (2013) A fast clustering-based feature subset selection algorithm for high-dimensional data. IEEE Trans Knowl Data Eng 25:1–14Google Scholar
  36. 36.
    Mandal M, Mukhopadhyay A (2013) Unsupervised non-redundant feature selection: a graph-theoretic approach. Springer, Berlin Heidelberg, pp 373–380Google Scholar
  37. 37.
    Bandyopadhyay S, Bhadra T, Mitra P, Maulik U (2014) Integration of dense subgraph finding with feature clustering for unsupervised feature selection. Pattern Recognit Lett 40(Supplement C):104–112Google Scholar
  38. 38.
    Moradi P, Rostami M (2015) A graph theoretic approach for unsupervised feature selection. Eng Appl Artif Intell 44:33–45Google Scholar
  39. 39.
    Kabir MM, Islam MM, Murase K (2010) A new wrapper feature selection approach using neural network. Neurocomputing 73(16):3273–3283Google Scholar
  40. 40.
    Pino Angulo A (2018) Gene selection for microarray cancer data classification by a novel rule-based algorithm. Information 9(1):6Google Scholar
  41. 41.
    Kannan SS, Ramaraj N (2010) A novel hybrid feature selection via symmetrical uncertainty ranking based local memetic search algorithm. Knowl-Based Syst 23(6):580–585Google Scholar
  42. 42.
    Zheng K, Wang X (2018) Feature selection method with joint maximal information entropy between features and class. Pattern Recognit 77:20–29Google Scholar
  43. 43.
    Moradi P, Rostami M (2015) Integration of graph clustering with ant colony optimization for feature selection. Knowl-Based Syst 84(Supplement C):144–161Google Scholar
  44. 44.
    Ghimatgar H, Kazemi K, Helfroush MS, Aarabi A (2018) An improved feature selection algorithm based on graph clustering and ant colony optimization. Knowl-Based Syst 159:270–285Google Scholar
  45. 45.
    Witten IH, Frank E, Hall MA (2011) Data mining: practical machine learning tools and techniques, 3rd edn. Morgan Kaufmann, AmsterdamGoogle Scholar
  46. 46.
    Ghasemzadeh H, Amini N, Saeedi R, Sarrafzadeh M (2015) Power-aware computing in wearable sensor networks: an optimal feature selection. IEEE Trans Mobile Comput 14(4):800–812Google Scholar
  47. 47.
    Bennasar M, Hicks Y, Setchi R (2015) Feature selection using joint mutual information maximisation. Expert Syst Appl 42(22):8520–8532Google Scholar
  48. 48.
    Cover T, Thomas J (2012) Elements of information theory. Wiley, New York, USAzbMATHGoogle Scholar
  49. 49.
    Le Martelot E, Hankin C (2013) Fast multi-scale detection of relevant communities in large-scale networks. Comput J 56(9):1136–1150Google Scholar
  50. 50.
    Blondel VD, Ioup Guillaume J, Lambiotte R, Lefebvre E (2008) Fast unfolding of communities in large networks. J Stat Mech Theory Exp 10(2008):P10008Google Scholar
  51. 51.
    Luby M (1986) A simple parallel algorithm for the maximal independent set problem. SIAM J Comput 15(4):1036–1053MathSciNetzbMATHGoogle Scholar
  52. 52.
    Yadav T, Sadhukhan K, Mallari RA (2016) Approximation algorithm for n-distance minimal vertex cover problem. arXiv preprint arXiv:1606.02889
  53. 53.
    Hippo Y, Taniguchi H, Tsutsumi S, Machida N, Chong JM, Fukayama M, Kodama T, Aburatani H (2002) Global gene expression analysis of gastric cancer by oligonucleotide microarrays. Cancer Res 62(1):233–240Google Scholar
  54. 54.
    Shipp MA, Ross KN, Tamayo P, Weng AP, Kutok JL, Aguiar RC, Gaasenbeek M, Angelo M, Reich M, Pinkus GS (2002) Diffuse large b-cell lymphoma outcome prediction by gene-expression profiling and supervised machine learning. Nat Med 8(1):68Google Scholar
  55. 55.
    Piloto S, Schilling TF (2010) Ovo1 links wnt signaling with n-cadherin localization during neural crest migration. Development dev-048439Google Scholar
  56. 56.
    Repository KRBDS kent ridge bio-medical dataset.
  57. 57.
    institute B (2014) Cancer program data aets.
  58. 58.
    Statnikov A, CF Aliferis, ITG (2005) Gene Expression Model Selector.
  59. 59.
    Zhu Z, Ong YS, Dash M (2007) Markov blanket-embedded genetic algorithm for gene selection. Pattern Recognit 40(11):3236–3248zbMATHGoogle Scholar
  60. 60.
    Zhu Z (2018) Cancer data sets.
  61. 61.
    Quinlan JR (1986) Induction of decision trees. Mach Learn 1Google Scholar
  62. 62.
    Obaidullah SM, Halder C, Santosh K, Das N, Roy K (2018) Phdindic\(\_11\): page-level handwritten document image dataset of 11 official indic scripts for script identification. Multimed Tools Appl 77(2):1643–1678Google Scholar
  63. 63.
    Cleophas TJ, Zwinderman AH (2015) Quantile-quantile plots, a good start for looking at your medical data (50 cholesterol measurements and 58 patients). Machine learning in medicine–a complete overview. Springer, Berlin, pp 253–259Google Scholar
  64. 64.
    Bouguelia MR, Nowaczyk S, Santosh K, Verikas A (2018) Agreeing to disagree: active learning with noisy labels without crowdsourcing. Int J Mach Learn Cybern 9(8):1307–1319Google Scholar
  65. 65.
    Bouguelia MR, Nowaczyk S, Payberah AH (2018) An adaptive algorithm for anomaly and novelty detection in evolving data streams. Data Min Knowl Discov 2018:1–37MathSciNetGoogle Scholar
  66. 66.
    Vajda S, Santosh K (2016) A fast k-nearest neighbor classifier using unsupervised clustering. In: International conference on recent trends in image processing and pattern Rrecognition, Springer, pp 185–193Google Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Soodeh Bakhshandeh
    • 1
  • Reza Azmi
    • 2
    Email author
  • Mohammad Teshnehlab
    • 3
  1. 1.Department of Computer Engineering, Science and Research BranchIslamic Azad UniversityTehranIran
  2. 2.Department of Computer EngineeringAlzahra UniversityTehranIran
  3. 3.Department of Control EngineeringK. N. Toosi University of TechnologyTehranIran

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