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Covering-based rough set classification system

  • New Trends in data pre-processing methods for signal and image classification
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Abstract

Medical data classification is applied in intelligent medical decision support system to classify diseases into different categories. Several classification methods are commonly used in various healthcare settings. These techniques are fit for enhancing the nature of prediction, initial identification of sicknesses and disease classification. The categorization complexities in healthcare area are focused around the consequence of healthcare data investigation or depiction of medicine by the healthcare professions. This study concentrates on applying uncertainty (i.e. rough set)-based pattern classification techniques for UCI healthcare data for the diagnosis of diseases from different patients. In this study, covering-based rough set classification (i.e. proposed pattern classification approach) is applied for UCI healthcare data. Proposed CRS gives effective results than delicate pattern classifier model. The results of applying the CRS classification method to UCI healthcare data analysis are based upon a variety of disease diagnoses. The execution of the proposed covering-based rough set classification is contrasted with other approaches, such as rough set (RS)-based classification methods, Kth nearest neighbour, improved bijective soft set, support vector machine, modified soft rough set and back propagation neural network methodologies using different evaluating measures.

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References

  1. Azar AT (2013) Fast neural network learning algorithms for medical applications. Neural Comput Appl 23(3–4):1019–1034. doi:10.1007/s00521-012-1026-y

    Article  Google Scholar 

  2. 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 

  3. 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 

  4. 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 

  5. 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 

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

    Article  Google Scholar 

  7. Kumar SS, Inbarani HH, Udhayakumar S (2014) Modified soft rough set for multiclass classification. Adv Intell Syst Comput 246:379–384

    Google Scholar 

  8. Udhaya Kumar S, Inbarani HH, Kumar SS (2013). Bijective soft set based classification of medical data. In: International conference on pattern recognition, informatics and medical engineering (PRIME), 1:517–521

  9. Udhaya Kumar S, Hannah Inbarani H, Senthil Kumar S (2014) Improved bijective-soft-set-based classification for gene expression data. Adv Intell Syst Comput 246:127–132

    Google Scholar 

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

    MATH  Google Scholar 

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

    Article  MATH  Google Scholar 

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

    Article  MathSciNet  Google Scholar 

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

    MathSciNet  MATH  Google Scholar 

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

    Article  Google Scholar 

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

    MathSciNet  MATH  Google Scholar 

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

    Article  MathSciNet  MATH  Google Scholar 

  17. Tsang ECC, Degang C, Yeung DS (2008) Approximations and reducts with covering generalized rough sets. Comput Math Appl 56(1):279–289

    Article  MathSciNet  MATH  Google Scholar 

  18. Yang T, Li Q (2010) Reduction about approximation spaces of covering generalized rough sets. Int J Approx Reason 51(3):335–345

    Article  MathSciNet  MATH  Google Scholar 

  19. Ma L (2012) On some types of neighborhood-related covering rough sets. Int J Approx Reason 53(6):901–911

    Article  MathSciNet  MATH  Google Scholar 

  20. Zhu W (2007) Topological approaches to covering rough sets. Inf Sci 177(6):1499–1508

    Article  MathSciNet  MATH  Google Scholar 

  21. Zhu W (2007) Generalized rough sets based on relations. Inf Sci 177(22):4997–5011

    Article  MathSciNet  MATH  Google Scholar 

  22. Zhu W (2009) Relationship between generalized rough sets based on binary relation and covering. Inf Sci 179(3):210–225

    Article  MathSciNet  MATH  Google Scholar 

  23. Zhu W (2009) Relationship among basic concepts in covering-based rough sets. Inf Sci 179(14):2478–2486

    Article  MathSciNet  MATH  Google Scholar 

  24. Yao Y, Yao B (2012) Covering based rough set approximations. Inf Sci 200(2012):91–107

    Article  MathSciNet  MATH  Google Scholar 

  25. Ge X, Bai X, Yun Z (2012) Topological characterizations of covering for special covering-based upper approximation operators. Inf Sci 204(2012):70–81

    Article  MathSciNet  MATH  Google Scholar 

  26. Wang C, Chen D, Sun B, Hu Q (2012) Communication between information systems with covering based rough sets. Inf Sci 216(2012):17–33

    Article  MathSciNet  MATH  Google Scholar 

  27. Medhat T (2012) Missing values via covering rough sets. Int J Data Min Intell Inf Technol Appl (IJMIA) 2(1):10–17

    Google Scholar 

  28. Sandeep YS, Reddy PVS, Manoj C, Lakkshmanan KA (2013) Identifying the vague regions by using covering based rough sets. Int J Adv Res Comput Sci Softw Eng 3(7):743–746

    Google Scholar 

  29. 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 

  30. 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 

  31. 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 

  32. Inbarani HH, Bagyamathi M, Azar AT (2015) A novel hybrid feature selection method based on rough set and improved harmony search. Neural Comput Appl 26(8):1859–1880. doi:10.1007/s00521-015-1840-0

    Article  Google Scholar 

  33. Azar AT, Inbarani HH, Devi KR (2016) Improved dominance rough set-based classification system. Neural Comput Appl 2016:1–16. doi:10.1007/s00521-016-2177-z

    Google Scholar 

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

  35. Elshazly HI, Azar AT, Elkorany AM, Hassanien AE (2013) Hybrid system based on rough sets and genetic algorithms for medical data classifications. Int J Fuzzy Syst Appl (IJFSA) 3(4):31–46

    Article  Google Scholar 

  36. Kumar S, Inbarani HH, Azar AT, Own HS, Balas VE (2014) Optimistic multi-granulation rough set based classification for neonatal jaundice diagnosis. Adv Intell Syst Comput (Soft Computing Applications) 356:307–317. doi:10.1007/978-3-319-18296-4_26

    MathSciNet  Google Scholar 

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

  38. Banu PKN, Inbarani HH, Azar AT, Hala S. Own HS, Hassanien AE (2014). Rough set based feature selection for Egyptian Neonatal Jaundice. In: AE Hassanien, M Tolba, AT Azar (eds.) Advanced machine learning technologies and applications: Second International Conference, AMLTA 2014, Cairo, Egypt, November 28–30, 2014. Proceedings, communications in computer and information science, vol 488, Springer GmbH Berlin/Heidelberg. ISBN: 978-3-319-13460-4

  39. Roy P, Goswami S, Chakraborty S, Azar AT, Dey N (2014) Image segmentation using rough set theory: a review. Int J Rough Sets Data Anal 1(2):62–74

    Article  Google Scholar 

  40. Jaganathan P, Kuppuchamy R (2013) A threshold fuzzy entropy based feature selection for medical database classification. Comput Biol Med 43(12):2222–2229

    Article  Google Scholar 

  41. Chang PC, Lin JJ, Liu CH (2012) An attribute weight assignment and particle swarm optimization algorithm for medical database classifications. Comput Methods Programs Biomed 107(3):382–392

    Article  Google Scholar 

  42. Seera M, Lim CP (2014) A hybrid intelligent system for medical data classification. Expert Syst Appl 41(5):2239–2249

    Article  Google Scholar 

  43. Dennis B, Muthukrishnan S (2014) AGFS: adaptive genetic fuzzy system for medical data classification. Appl Soft Comput 25:242–252

    Article  Google Scholar 

  44. Polat K, Günes S (2007) An improved approach to medical data sets classification: artificial immune recognition system with fuzzy resource allocation mechanism. Expert Systems 24(4):252–270

    Article  Google Scholar 

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

  46. Kumar SS, Hannah Inbarani H (2015) Optimistic multi-granulation rough set based classification for medical diagnosis. Proc Comput Sci 47:374–382

    Article  Google Scholar 

  47. Kumar SS, Inbarani HH, Azar AT, Hala SO, Balas VE, Olariu T (2015) Optimistic multi-granulation rough set based classification for neonatal jaundice diagnosis. Adv Intell Syst Comput 356:307–317

    MathSciNet  Google Scholar 

  48. Gadaras I, Mikhailov L (2009) An interpretable fuzzy rule-based classification methodology for medical diagnosis. Artif Intell Med 47(1):25–41

    Article  Google Scholar 

  49. Tomczak JM, Zieba M (2015) Probabilistic combination of classification rules and its application to medical diagnosis. Mach Learn 101:105–135

    Article  MathSciNet  MATH  Google Scholar 

  50. Neshat M, Sargolzaei M, Nadjaran Toosi A, Masoumi A (2012) Hepatitis disease diagnosis using hybrid case based reasoning and particle swarm optimization. In: ISRN Artificial Intelligence, vol 2012

  51. Lin KC, Hsieh YH (2015) Classification of medical datasets using SVMs with hybrid evolutionary algorithms based on endocrine-based particle swarm optimization and artificial bee colony algorithms. J Med Syst 119:1–9

    Google Scholar 

  52. AlMuhaideb Sarab, Menai MEB (2014) HColonies: a new hybrid meta-heuristic for medical data classification. J Appl Intell 41:282–298

    Article  Google Scholar 

  53. Dash T, Nayak SK, Behera HS (2015) Hybrid gravitational search and particle swarm based fuzzy MLP for medical data classification. Comput Intell Data Min 1:35–43

    Google Scholar 

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Authors and Affiliations

  1. Department of Computer Science, Periyar University, Salem, Tamil Nadu, 636 011, India

    S. Senthil Kumar & H. Hannah Inbarani

  2. Faculty of Computers and Information, Benha University, Banha, Egypt

    Ahmad Taher Azar

  3. Department of Electrical and Electronics Engineering, Faculty of Engineering and Architecture, Abant Izzet Baysal University, 14280, Bolu, Turkey

    Kemal Polat

Authors
  1. S. Senthil Kumar
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  2. H. Hannah Inbarani
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  3. Ahmad Taher Azar
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  4. Kemal Polat
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Correspondence to Ahmad Taher Azar.

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Senthil Kumar, S., Hannah Inbarani, H., Azar, A.T. et al. Covering-based rough set classification system. Neural Comput & Applic 28, 2879–2888 (2017). https://doi.org/10.1007/s00521-016-2412-7

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