Improved dominance rough set-based classification system

Abstract

Feature selection and classification is widely used in many areas of science and engineering, as large datasets become increasingly common. In particular, bioscience and medical datasets routinely contain several thousands of features. For effective data mining in such databases, many methods and techniques have been developed. Rough set is a mathematical theory for dealing with uncertainty. In dominance-based rough set extension of rough set, the set of objects partitioned into pre-defined and preference-ordered classes, the new rough set approach is able to approximate this partition by means of dominance relations. This paper suggests improved dominance-based rough set for classification of medical data. Dominance-based rough set can handle ordinal attribute. This paper proposed a technique for applying dominance-based rough set for nominal attribute. This proposed work suggests decision table to determine dominance relation, and then improved dominance-based rough set is applied to find lower, upper, boundary approximations in the entire dataset. Then attribute reduction based on proposed technique is applied to find the essential attribute required for classification. This proposed method can accurately classify medical datasets collected from UCI repository Web sites. This proposed method works in seven different datasets: They are heart disease dataset, Pima Indian diabetes dataset, Breast cancer Wisconsin dataset, heart valve dataset, jaundice datasets, dermatology dataset and lung cancer dataset. Comparing the classification accuracy with rule-based classifier (Zero R, decision table), tree-based classifier (J48, Random forest, Random Tree), neural network-based classifier (multilayer perceptron), lazy classifier (IBk, KStar, LWL), Bayesian-based classifier (Naïve Bayes), benchmark algorithm k-nearest–neighbour, and classical rough set approach, improved dominance-based rough set gives higher accuracy.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2

References

  1. 1.

    Abdel-AalM RE (2005) GMDH-based feature ranking and selection for improved classification of medical data. J Biomed Inform 38(6):456–468

    Article  Google Scholar 

  2. 2.

    An L, Chen Z, Tong L (2011) Generation and application of decision rules within dominance-based rough set approach to multicriteria sorting. Int J Innov Comput Inf Control 7(3):1145–1155

    Google Scholar 

  3. 3.

    Anaraki JR, Eftekhari M (2013) Rough set based feature selection: a review. In: Fifth conference on information and knowledge technology (IKT), 28–30 May 2013. IEEE, pp 301–306. doi:10.1109/IKT.2013.6620083

  4. 4.

    Azar AT (2014) Neuro-fuzzy feature selection approach based on linguistic hedges for medical diagnosis. Int J Model Identif Control (IJMIC) 22(3):195–206. doi:10.1504/IJMIC.2014.065338

    Article  Google Scholar 

  5. 5.

    Azar AT, Hassanien AE (2014) Dimensionality reduction of medical big data using neural-fuzzy classifier. Soft Comput 19(4):1115–1127. doi:10.1007/s00500-014-1327-4

    Article  Google Scholar 

  6. 6.

    Azar AT, Balas VE, Olariu T (2014a) Classification of EEG-based brain–computer interfaces. In: Advanced Intelligent Computational Technologies and Decision Support Systems, Studies in Computational Intelligence, vol 486. pp 97–106. doi:10.1007/978-3-319-00467-9_9

  7. 7.

    Azar AT, Ali HS, Balas VE (2014b) Boosted decision trees for vertebral column disease diagnosis. In: Soft computing applications, volume 356 of the series advances in intelligent systems and computing, springer. Springer, pp 319–333. doi:10.1007/978-3-319-18296-4_27

  8. 8.

    Azar AT, El-Said SA (2014) Performance analysis of support vector machines classifiers in breast cancer mammography recognition. Neural Comput Appl 24(5):1163–1177. doi:10.1007/s00521-012-1324-4

    Article  Google Scholar 

  9. 9.

    Azar AT, El-Said SA (2013) Probabilistic neural network for breast cancer classification. Neural Comput Appl 23(6):1737–1751. doi:10.1007/s00521-012-1134-8

    Article  Google Scholar 

  10. 10.

    Azar AT, El-Said SA (2013) Superior neuro-fuzzy classification systems. Neural Comput Appl 23(1):55–72. doi:10.1007/s00521-012-1231-8

    Article  Google Scholar 

  11. 11.

    Azar AT, El-Metwally SM (2013) Decision tree classifiers for automated medical diagnosis. Neural Comput Appl 23(7–8):2387–2403. doi:10.1007/s00521-012-1196-7

    Article  Google Scholar 

  12. 12.

    Azar AT, El-Said SA, Balas VE, Olariu T (2013a) Linguistic hedges fuzzy feature selection for erythemato-squamous diseases. In: Soft computing applications, advances in intelligent systems and computing (AISC), vol 195, Springer, Berlin, pp 487–500. doi:10.1007/978-3-642-33941-7_43

  13. 13.

    Azar AT, Banu PKN, Inbarani HH (2013b). PSORR—an unsupervised feature selection technique for fetal heart rate. In: 5th international conference on modelling, identification and control (ICMIC 2013), 31 Aug, 1–2 Sept 2013, Egypt

  14. 14.

    Azar AT, Hassanien AE, Kim TH (2012) Expert system based on neural-fuzzy rules for thyroid diseases diagnosis. In: International conference on bio-science and bio-technology (BSBT 2012), 16–19 Dec 2012, Korea. Communications in Computer and Information Science series, vol 353. Springer, pp 94–105. ISBN: 978-3-642-35520-2. doi:10.1007/978-3-642-35521-9_13

  15. 15.

    Basu T, Murthy CA (2012) Effective text classification by a supervised feature selection approach In: IEEE 12th international conference on data mining workshops (ICDMW), 10–10 Dec 2012. Brussels, pp 918–925. doi:10.1109/ICDMW.2012.45

  16. 16.

    Banu PKN, Inbarani HH, Azar AT, Hala S. Own HS, Hassanien AE (2014) Rough set based feature selection for egyptian neonatal jaundice. In: Hassanien AE, Tolba M, Azar AT (eds) Advanced machine learning technologies and applications: second international conference, AMLTA 2014, Cairo, Egypt, 28–30 Nov 2014. Proceedings, communications in computer and information science, vol 488. Springer, Berlin. ISBN: 978-3-319-13460-4

  17. 17.

    Bello R, Gómez Y, Caballero Y, Nowe A, Falcón R (2009) Rough sets and evolutionary computation to solve the feature selection problem. In: Abraham A, Falcón R, Bello R (eds) Rough set theory: a true landmark in data analysis, studies in computational intelligence, vol 174. Springer, Berlin, pp 235–260

    Google Scholar 

  18. 18.

    Boudreau-Trudel B, Zaras K (2012) Comparison of analytic hierarchy process and dominance-based rough set approach as multi-criteria decision aid methods for the selection of investment projects. Am J Ind Bus Manag 2(1):7–12

    Google Scholar 

  19. 19.

    Chakhar S, Saad I (2012) Dominance-based rough set approach for groups in multicriteria classification problems. Decis Support Syst 54(1):372–380

    Article  Google Scholar 

  20. 20.

    Du WS, Hu BQ (2015) Allocation reductions in inconsistent decision tables based on dominance relations. Fuzzy Inf Eng 7(3):259–273

    MathSciNet  Article  Google Scholar 

  21. 21.

    Elshazly HI, Elkorany AM, Hassanien AE, Azar AT (2013) Ensemble classifiers for biomedical data: performance evaluation. In: IEEE 8th international conference on computer engineering and systems (ICCES), 26–28 Nov 2013. Ain Shams University, pp 184–189. doi:10.1109/ICCES.2013.6707198. Print ISBN: 978-1-4799-0078-7

  22. 22.

    Fan TF, Liau CJ, Liu DR (2011) Dominance-based fuzzy rough set analysis of uncertain and possibilistic data tables. Int J Approx Reason 52(9):1283–1297

    MathSciNet  Article  MATH  Google Scholar 

  23. 23.

    Fazayeli F, Wang L, Mandziuk J (2008) Feature selection based on the rough set theory and EM clustering algorithm. In: Rough sets and current trends in computing. Lecture notes in computer science, vol 5306. Springer, pp 272–282

  24. 24.

    Greco S, Słowiński R, Zielniewicz P (2013) Putting dominance-based rough set approach and robust ordinal regression together. Decis Support Syst 54(2):891–903

    Article  Google Scholar 

  25. 25.

    Greco S, Matarazzo B, Slowinski R (1998) A new rough set approach to evaluation of bankruptcy risk. In: Zopounidis C (ed) Operational tools in the management of financial risk. Springer, Berlin, pp 121–136

    Google Scholar 

  26. 26.

    Greco S, Matarazzo B, Slowinski R (1999) Rough approximation of preference relation by dominance relations. Eur J Oper Res 117(1):63–83

    Article  MATH  Google Scholar 

  27. 27.

    Greco S, Matarazzo B, Slowinski R (1999) The use of rough sets and fuzzy sets in MCDM. In: Gal T, Stewart TJ, Hanne T (eds) Multicriteria decision making. International series in operations research and management science, vol 21. Springer, Berlin, pp 397–455

    Google Scholar 

  28. 28.

    Greco S, Matarazzo B, Słowiński R (2006) Dominance-based rough set approach to case-based reasoning. In: Torra V, Narukawa Y, Valls A, Domingo-Ferrer J (eds) Modeling decisions for artificial intelligence. Lecture notes in artificial intelligence, vol 3885. Springer, Berlin, pp 7–18

    Google Scholar 

  29. 29.

    Greco S, Matarazzo B, Słowiński R (2002) Multicriteria classification by dominance-based rough set approach—methodological basis of the 4eMka system. In: Kloesgen W, Zytkow J (eds) Handbook of data mining and knowledge discovery. Oxford University Press, New York

    Google Scholar 

  30. 30.

    Hassanien AE, Azar AT, Snasel V, Kacprzyk J, Abawajy JH (2015) Big data in complex systems: challenges and opportunities, studies in big data, vol 9. Springer, Berlin. ISBN 978-3-319-11055-4

  31. 31.

    Hassanien AE, Tolba M, Azar AT (2014a) Advanced machine learning technologies and applications: second international conference, AMLTA 2014, Cairo, Egypt, 28–30 Nov, 2014. Proceedings, communications in computer and information science, vol 488. Springer, Berlin. ISBN: 978-3-319-13460-4

  32. 32.

    Hassanien AE, Moftah HM, Azar AT, Shoman M (2014) MRI breast cancer diagnosis hybrid approach using adaptive ant-based segmentation and multilayer perceptron neural networks classifier. Appl Soft Comput 14(Part A):62–71

    Article  Google Scholar 

  33. 33.

    Hassanien AE (2004) Rough set approach for attribute reduction and rule generation: a case of patients with suspected breast cancer. J Am Soc Inf Sci Technol 55(11):954–962

    Article  Google Scholar 

  34. 34.

    Hassanien AE, Ali JMH (2004) Enhanced rough sets rule reduction algorithm for classification digital mammography. Intell Syst J 13(2):151–171

    Google Scholar 

  35. 35.

    Huang B, Wei D, Li H, Zhuang Y (2013) Using a rough set model to extract rules in dominance-based interval-valued intuitionistic fuzzy information systems. Inf Sci 221:215–229

    MathSciNet  Article  MATH  Google Scholar 

  36. 36.

    Inbarani HH, Bagyamathi M, Azar AT (2015) A novel hybrid feature selection method based on rough set and improved harmony search. Neural Comput Appl. doi:10.1007/s00521-015-1840-0

    Google Scholar 

  37. 37.

    Inbarani HH, Kumar SS, Azar AT, Hassanien AE (2015) Hybrid TRS-PSO clustering approach for Web2.0 social tagging system. Int J Rough Sets Data Anal (IJRSDA) 2(1):22–37

    Article  Google Scholar 

  38. 38.

    Inbarani HH, Kumar SS, Azar AT, Hassanien AE (2014a) Soft rough sets for heart valve disease diagnosis. In: Hassanien AE, Tolba M, Azar AT (eds) Advanced machine learning technologies and applications: second international conference, AMLTA 2014, Cairo, Egypt, 28–30 Nov 2014. Proceedings, communications in computer and information science, vol 488. Springer, Berlin. ISBN: 978-3-319-13460-4

  39. 39.

    Inbarani HH, Banu PKN, Azar AT (2014) Feature selection using swarm-based relative reduct technique for fetal heart rate. Neural Comput Appl 25(3–4):793–806. doi:10.1007/s00521-014-1552-x

    Article  Google Scholar 

  40. 40.

    Inbarani HH, Azar AT, Jothi G (2014) Supervised hybrid feature selection based on PSO and rough sets for medical diagnosis. Comput Methods Programs Biomed 113(1):175–185

    Article  Google Scholar 

  41. 41.

    Inuiguchi M, Yoshioka Y, Kusunoki Y (2009) Variable-precision dominance-based rough set approach and attribute reduction. Int J Approx Reason 50(8):1199–1214

    MathSciNet  Article  MATH  Google Scholar 

  42. 42.

    Jothi G, Inbarani HH, Azar AT (2013) Hybrid tolerance-PSO based supervised feature selection for digital mammogram images. Int J Fuzzy Syst Appl (IJFSA) 3(4):15–30

    Article  Google Scholar 

  43. 43.

    Kumar SU, Inbarani HH, Azar AT (2015) Hybrid bijective soft set—neural network for ECG arrhythmia classification. Int J Hybrid Intell Syst 12(2):103–118

    Article  Google Scholar 

  44. 44.

    Kumar SU, Inbarani HH, Azar AT, Own HS, Balas VE (2014). Optimistic multi-granulation rough set based classification for neonatal jaundice diagnosis. In: Soft computing applications, volume 356 of the series advances in intelligent systems and computing. Springer, pp 307–317. doi:10.1007/978-3-319-18296-4_26

  45. 45.

    Kumar SS, Inbarani HH, Azar AT, Hassanien AE (2015) Rough set based meta-heuristic clustering approach for social e-learning systems. Int J Intell Eng Inform 3(1):23–41

    Google Scholar 

  46. 46.

    Kusunoki Y, Inuiguchi M (2010) A unified approach to reducts in dominance-based rough set approach. Soft Comput 14(5):507–515

    Article  MATH  Google Scholar 

  47. 47.

    Leung Y, Fischer MM, Wu WZ, Mid JS (2008) A rough set approach for the discovery of classification rules in interval-valued information systems. Int J Approx Reason 47(2):233–246

    MathSciNet  Article  MATH  Google Scholar 

  48. 48.

    Leung Y, Wu ZW, Zhang WZ (2006) Knowledge acquisition in incomplete information systems: a rough set approach. Eur J Oper Res 168(1):164–180

    MathSciNet  Article  MATH  Google Scholar 

  49. 49.

    Lichman M (2013) UCI machine learning repository [http://archive.ics.uci.edu/ml]. University of California, School of Information and Computer Science, Irvine, CA

  50. 50.

    Li S, Li T, Zhang Z, Chen H, Zhang J (2015) Parallel computing of approximations in dominance-based rough sets approach. Knowl-Based Syst 87:102–111

    Article  Google Scholar 

  51. 51.

    Li HL, Chen MH (2008) Induction of multiple criteria optimal classification rules for biological and medical data. Comput Biol Med 38(1):42–52

    MathSciNet  Article  Google Scholar 

  52. 52.

    Li S, Li T (2014) A parallel matrix based approach for computing approximations in dominance based rough set approach. In: Miao D, Pedrycz W, Slezak D, Peters G, Hu Q, Wang R (eds) Rough sets and knowledge technology. Lecture notes in computer science, vol 8818. Springer, Berlin, pp 173–183

    Google Scholar 

  53. 53.

    Luo G, Yang X (2010) Limited dominance-based rough set model and knowledge reductions in incomplete decision system. J Inf Sci Eng 26:2199–2211

    MathSciNet  Google Scholar 

  54. 54.

    Maglogiannis I, Loukis E, Zafiropoulos E, Stasis A (2009) Support vectors machine-based identification of heart valve diseases using heart sounds. Comput Methods Programs Biomed 95(1):47–61

    Article  Google Scholar 

  55. 55.

    Moftah HM, Azar AT, Al-Shammari ET, Ghali NI, Hassanien AE, Shoman M (2014) Adaptive k-means clustering algorithm for mr breast image segmentation. Neural Comput Appl 24(7–8):1917–1928. doi:10.1007/s00521-013-1437-4

    Article  Google Scholar 

  56. 56.

    Pancerz K (2014) Dominance-based rough set approach for decision systems over ontological graphs. In: Proceedings of the federated conference on computer science and information systems. pp 323–330. ISBN 978-83-60810-51-4

  57. 57.

    Pawlak Z (1982) Rough sets. Int J Parallel Prog 11(5):341–356

    MATH  Google Scholar 

  58. 58.

    Pawlak Z, Slowinski R (1994) Decision analysis using rough sets. Int Trans Oper Res 1(1):107–114

    Article  MATH  Google Scholar 

  59. 59.

    Pawlak Z (1995) Vagueness and uncertainty: a rough set perspective. Comput Intell 11(2):227–232

    MathSciNet  Article  Google Scholar 

  60. 60.

    Pawlak Z (1996) Rough sets: present state and the future. Found Comput Decis Sci 18(3–4):157–166

    MathSciNet  MATH  Google Scholar 

  61. 61.

    Pawlak Z (1999) Rough classification. Int J Hum Comput Stud 51(2):369–383

    Article  Google Scholar 

  62. 62.

    Pawlak Z (2002) Rough sets and intelligent data analysis. Inf Sci 147(1–4):1–12

    MathSciNet  Article  MATH  Google Scholar 

  63. 63.

    Pawlak Z, Skowron A (2007) Rough sets and Boolean reasoning. Inf Sci 177(1):41–73

    MathSciNet  Article  MATH  Google Scholar 

  64. 64.

    Polkowski L and Artiemjew P (2008) Rough mereology in classification of data: voting by means of residual rough inclusions. In: Chan CC, Grzymala-Busse JW, Ziarko WP (eds) Rough sets and current trends in computing, volume 5306 of the series lecture notes in computer science. pp 113–120

  65. 65.

    Saeys Y, Inza I, Larranaga P (2007) A review of feature selection techniques. Bioinformatics 23(19):2507–2517

    Article  Google Scholar 

  66. 66.

    Szelazg M, Greco S, Słowiński R (2014) Variable consistency dominance-based rough set approach to preference learning in multicriteria ranking. Inf Sci 277(2014):525–552

    MathSciNet  Article  MATH  Google Scholar 

  67. 67.

    Swiniarski RW, Skowron A (2003) Rough set method in feature selection and recognition. Pattern Recogn Lett 24(6):833–849

    Article  MATH  Google Scholar 

  68. 68.

    Tsang ECC, Zhao SY, Yeung DS, Lee JWT (2006) Learning from an incomplete information system with continuous-valued attributes by a rough set technique. Proc Int Conf Mach Learn Cybern LNAI 3930(36):568–577

    Google Scholar 

  69. 69.

    Thangavel K, Pethalakshmi A (2009) Dimensionality reduction based on rough set theory: a review. Appl Soft Comput 9(1):1–12

    Article  Google Scholar 

  70. 70.

    Wang P (2007) Highly scalable rough set reducts generation. J Inf Sci Eng 23(4):1281–1298

    Google Scholar 

  71. 71.

    Wang X, Gotoh O (2009) Accurate molecular classification of cancer using simple rules. BMC Med Genom 2(64):1–23

    Google Scholar 

  72. 72.

    Wu WZ, Zhang WX, Li HZ (2003) Knowledge acquisition in incomplete fuzzy information systems via the rough set approach. Expert Syst 20(5):280–286

    Article  Google Scholar 

  73. 73.

    Xiaoyan Z, Weihua X (2012) Fuzzy rough set based on dominance relations. In: Z. Qian, L. Cao, W. Su, T. Wang, H. Yang (eds.) Recent advances in computer science and information engineering, lecture notes in electrical engineering, vol 125. pp 119–125

  74. 74.

    Xu W, Liu S, Zhang W (2013) Lattice-valued information systems based on dominance relation. Int J Mach Learn Cybernet 4(3):245–257

    Article  Google Scholar 

  75. 75.

    Zhang M, Yao JT (2004) A rough sets based approach to feature selection. Proceedings NAFIPS ‘04, IEEE annual meeting of the fuzzy information, 2004, vol 1. pp 434–439. doi:10.1109/NAFIPS.2004.1336322

  76. 76.

    Zhang X, Chen D (2014) Generalized dominance based rough set model for the dominance intutionistic fuzzy information systems. In: Miao D, Pedrycz W, Slezak D, Peters G, Hu Q, Wang R (eds) Rough sets and knowledge technology, lecture notes in computer science, vol 8818, pp 3–14

  77. 77.

    Zhai LY, Khoo LP, Zhong ZW (2009) A dominance-based rough set approach to Kansei Engineering in product development. Expert Syst Appl 36(1):393–402

    Article  Google Scholar 

Download references

Author information

Affiliations

Authors

Corresponding author

Correspondence to Ahmad Taher Azar.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Azar, A.T., Inbarani, H.H. & Renuga Devi, K. Improved dominance rough set-based classification system. Neural Comput & Applic 28, 2231–2246 (2017). https://doi.org/10.1007/s00521-016-2177-z

Download citation

Keywords

  • Dominance-based rough set
  • Decision table
  • Feature selection
  • Classification