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RETRACTED ARTICLE: Tolerance rough set firefly-based quick reduct

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This article was retracted on 02 January 2023

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Abstract

In medical information system, there are a lot of features and the relationship among elements is solid. In this way, feature selection of medical datasets gets awesome worry as of late. In this article, tolerance rough set firefly-based quick reduct, is developed and connected to issue of differential finding of diseases. The hybrid intelligent framework intends to exploit the advantages of the fundamental models and, in the meantime, direct their restrictions. Feature selection is procedure for distinguishing ideal feature subset of the original features. A definitive point of feature selection is to build the precision, computational proficiency and adaptability of expectation strategy in machine learning, design acknowledgment and information mining applications. Along these lines, the learning framework gets a brief structure without lessening the prescient precision by utilizing just the chose remarkable features. In this research, a hybridization of two procedures, tolerance rough set and as of late created meta-heuristic enhancement calculation, the firefly algorithm is utilized to choose the conspicuous features of medicinal information to have the capacity to characterize and analyze real sicknesses. The exploratory results exhibited that the proficiency of the proposed system outflanks the current supervised feature selection techniques.

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References

  1. Hassanien AE, Abraham A, Peters JF, Schaefer G (2009) Rough sets in medical informatics applications. In: Mehnen J, Köppen M, Saad A, Tiwari A (eds) Applications of soft computing. Springer, Berlin, pp 23–30

    Book  Google Scholar 

  2. Wang Y, Ma L (2009) Feature selection for medical dataset using rough set theory. In: Proceedings of the 3rd WSEAS international conference on computer engineering and applications. World Scientific and Engineering Academy and Society (WSEAS)

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

    Article  MathSciNet  Google Scholar 

  4. Fu X, Tan F, Wang H, Zhang Y-Q, Harrison R (2006) Feature similarity based redundancy reduction for gene selection. In: Conference on data mining

  5. Kohavi R, John GH (1997) Wrappers for feature subset selection. Artif Intell 7:273–323

    Article  MATH  Google Scholar 

  6. Huang J, Cai Y, Xu X (2007) A hybrid genetic algorithm for feature selection wrapper based on mutual information. Pattern Recogn Lett 28:1825–1844

    Article  Google Scholar 

  7. Sivagaminathan RK, Ramakrishnan S (2007) A hybrid approach for feature subset selection using neural networks and ant colony optimization. Expert Syst Appl 33:49–60

    Article  Google Scholar 

  8. Parthaláin NM, Shen Q (2009) Exploring the boundary region of tolerance rough sets for feature selection. Pattern Recognit Elsevier 42:655–667

    Article  MATH  Google Scholar 

  9. Yang X (2009) Firefly algorithm for multimodal optimization. In: SAGA. Lecture notes in computer science, pp 169–178

  10. Zang H, Zhang S, Hapeshi K (2010) A review of nature-inspired algorithms. J Bionic Eng 7:S232–S237

    Article  Google Scholar 

  11. 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

    Google Scholar 

  12. 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 

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

    Article  Google Scholar 

  14. Azar AT, Kumar SS, Inbarani HH, Hassanien AE (2016) Pessimistic multi-granulation rough set based classification for heart valve disease diagnosis. Int J Model Identif Control (IJMIC) 26(1):42–51

    Article  Google Scholar 

  15. Kumar SS, Inbarani HH, Azar AT, Polat K (2016) Covering rough set based classification system. Neural Comput Appl. doi:10.1007/s00521-016-2412-7(Springer)

    Article  Google Scholar 

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

  17. Azar AT, Vashist R, Vashishtha A (2015) A rough set based total quality management approach in higher education. In: Zhu Q, Azar AT (eds) Complex system modelling and control through intelligent soft computations. Studies in fuzziness and soft computing, vol 319. Springer, Germany, pp 389–406. doi:10.1007/978-3-319-12883-2_14

  18. Azar AT, Bouaynaya N, Polikar R (2015) Inductive learning based on rough set theory for medical decision making. In: IEEE international conference on fuzzy systems (FUZZ-IEEE), 2–5 Aug 2015, Istanbul, pp 1–8. doi:10.1109/FUZZ-IEEE.2015.7338075

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

  20. Yang X-S (2010) Nature-inspired metaheuristic algorithms, 2nd edn. Luniver Press, Bristol

    Google Scholar 

  21. Liu H, Motoda H (2007) Computational methods of feature selection. CRC Press, Boca Raton

    Book  MATH  Google Scholar 

  22. Talbi E (2009) Metaheuristics: from design to implementation. Wiley, Hoboken

    Book  MATH  Google Scholar 

  23. Yang X-S (2010) Firefly algorithm, Lévy flights and global optimization. In: Bramer M et al (eds) Research and development in intelligent systems, vol XXVI. Springer, London, pp 209–218

    Chapter  Google Scholar 

  24. Skowron A, Stepaniuk J (1996) Tolerance approximation spaces. Fundam Inform 27:245–253

    Article  MathSciNet  MATH  Google Scholar 

  25. Jensen R, Shen Q (2007) Rough set based feature selection: a review. In: Hassanien AE, Suraj Z, Slezak D, Lingras P (eds) Rough computing: theories technologies and applications, IGI-Global, USA, pp 70–107

    Google Scholar 

  26. Stepaniuk JS, Kobayashi S, Yokomori T, Tanaka H (1996) Similarity based rough sets and learning. In: Tsumoto (ed) Proceedings of the 4th international workshop on rough sets, fuzzy sets and machine discovery, Tokyo, pp 18–22

  27. 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

    Google Scholar 

  28. Banati H, Bajaj M (2011) Fire fly based feature selection approach. Int J Comput Sci Issues 8:473–480. http://www.ics.uci.edu/~mlearn/

  29. Inbarani HH, Banu PN (2012) Unsupervised feature selection using tolerance rough set based relative reduct. In: 2012 international conference on advances in engineering, science and management (ICAESM). IEEE, pp 326–331

  30. Jothi G, Inbarani HH (2016) Hybrid tolerance rough set-firefly-based supervised feature selection for MRI brain tumor image classification. Appl Soft Comput 46:639–651

    Article  Google Scholar 

  31. Own HS, Abraham A (2012) A new weighted rough set framework based classification for Egyptian NeoNatal Jaundice. Appl Soft Comput 12(3):999–1005

    Article  Google Scholar 

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

  33. Witten IH, Frank E, Hall MA (2000) Data mining: practical machine learning tools and techniques. Morgan Kaufmann Publishers, San Francisco, CA, USA. ISBN:0123748569 9780123748560

    Google Scholar 

  34. Olson DL, Delen D (2008) Advanced data mining techniques, 1st edn. Springer, Berlin (ISBN:3-540-76916-1, p 138)

    MATH  Google Scholar 

  35. Hall MA (1999) Correlation based feature selection for machine learning. Ph.D. thesis, Department of Computer Science, University of Waikato

  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 26:1859–1880. doi:10.1007/s00521-015-1840-0

    Article  Google Scholar 

  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. 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 

  39. 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, 28–30 Nov 2014. Proceedings, communications in computer and information science, vol 488. Springer, GmbH Berlin, pp 367–378. ISBN:978-3-319-13460-4

  40. Inbarani HH, Kumar SS, Azar AT, Hassanien AE (2014) 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. Proceedings, communications in computer and information science, vol 488. Springer, GmbH Berlin, 28–30 Nov 2014. ISBN:978-3-319-13460-4

  41. 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 Aug–1–2 Sept 2013, Egypt

  42. Srivastava A, Chakrabarti S, Das S, Ghosh S, Jayaraman VK (2013) Hybrid firefly-based simultaneous gene selection and cancer classification using support vector machines and random forests. In: Proceedings of 7th international conference on bio-inspired computing: theories and applications (BIC-TA 2012). Springer, pp 485–494

  43. Seera M, Lim CP (2013) A hybrid intelligent system for medical data classification. Expert Syst Appl 41(5):2239–2249

    Article  Google Scholar 

  44. Aroquiaraj IL, Thangavel K (2012) Unsupervised feature selection in digital mammogram image using tolerance rough set based quick reduct and relative reduct. In: International conference computational intelligence and communication networks (CICN), pp 436–440

  45. Jothi G, Inbarani HH (2012) Soft set based quick reduct approach for unsupervised feature selection. In: 2012 IEEE international conference on advanced communication control and computing technologies (ICACCCT). IEEE, pp 277–281

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

    Article  Google Scholar 

  47. Mansor MN, Yaacob S, Muthusamy H, Nisha S (2011) PCA-based feature extraction and k-NN algorithm for early jaundice detection. Int J Soft Comput Softw Eng (JSCSE) 1(1):25–29

    Google Scholar 

  48. Gandomi AH, Yang XS, Alavi AH (2011) Mixed variable structural optimization using firefly algorithm. Comput Struct 89(23):2325–2336

    Article  Google Scholar 

  49. Abshouri AA, Bakhtiary A (2012) A new clustering method based on firefly and KHM. J Commun Comput 9(4):387–391

    Google Scholar 

  50. Senthilnath J, Omkar SN, Mani V (2011) Clustering using firefly algorithm: performance study. Swarm Evolut Comput 1:164–171

    Article  Google Scholar 

  51. Huang SH, Wulsin LR, Li H, Guo J (2009) Dimensionality reduction for knowledge discovery in medical claims database: application to antidepressant medication utilization study. Comput Methods Programs Biomed 93:115–123

    Article  Google Scholar 

  52. Wang X, Yang J, Jensen R, Liu X (2006) Rough set feature selection and rule induction for prediction of malignancy degree in brain glioma. Comput Methods Programs Biomed 83:147–156

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Information Technology, Sona College of Technology (Autonomous), Salem, Tamil Nadu, 636 005, India

    Jothi Ganesan

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

    Hannah H. Inbarani

  3. Faculty of Computers and Information, Benha University, Benha, Egypt

    Ahmad Taher Azar

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

    Kemal Polat

Authors
  1. Jothi Ganesan
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  2. Hannah H. Inbarani
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  3. Ahmad Taher Azar
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  4. Kemal Polat
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Corresponding author

Correspondence to Ahmad Taher Azar.

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All authors declare that they have no conflict of interest.

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This article does not contain any studies with human participants performed by any of the authors.

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Ganesan, J., Inbarani, H.H., Azar, A.T. et al. RETRACTED ARTICLE: Tolerance rough set firefly-based quick reduct. Neural Comput & Applic 28, 2995–3008 (2017). https://doi.org/10.1007/s00521-016-2514-2

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  • DOI: https://doi.org/10.1007/s00521-016-2514-2

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