Optimizing the modified fuzzy ant-miner for efficient medical diagnosis

Abstract

The advantage of efficient searches belonging to ant-miner over several other approaches leads to prominent achievements on rules mining. Fuzzy ant-miner, an extension of the ant-miner provides a fuzzy mining framework for the automatic extraction of fuzzy rules from labeled numerical data. However, it is easily trapped in local optimal, especially when it applies to medical cases, where real world accuracy is elusive; and the interpretation and integration of medical knowledge is necessary. In order to relieve such a local optimal difficulty, this paper proposes OMFAM which applies simulated annealing to optimize fuzzy set parameters associated with a modified fuzzy ant-miner (MFAM). MFAM employs attributes and training case weighting. The proposed method, OMFAM was experimented with six critical medical cases for developing efficient medical diagnosis systems. The performance measurement relates to accuracy as well as interpretability of the mined rules. The performance of the OMFAM is compared with such references as MFAM, fuzzy ant-miner (FAM), and other classification methods. At last, it indicates the superiority of the OMFAM algorithm over the others.

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References

  1. 1.

    Michalski RS, Bratko I, Kubat M (1998) Machine learning and data mining: methods and applications. Wiley, New York

    Google Scholar 

  2. 2.

    Steimann F (2001) On the use and usefulness of fuzzy sets in medical AI. Artif Intell Med 21:131–137

    Article  Google Scholar 

  3. 3.

    Leung KS, Felix Wong WS, Lam W (1989) Applications of a novel fuzzy expert system shell. Expert Syst 6:2–10. doi:10.1111/j.1468-0394.1989.tb00070.x

    Article  Google Scholar 

  4. 4.

    Liao SH (2005) Expert systems methodologies and applications—a decade review form 1995 to 2004. Expert Syst Appl 28:93–103. doi:10.1016/j.eswa.2004.08.003

    Article  Google Scholar 

  5. 5.

    Ilias M, Elias Z, Ioannis A (2009) An intelligent system for automated breast cancer diagnosis and prognosis using SVM based classifiers. Appl Intell 30(1):24–36. doi:10.1007/s10489-007-0073-z

    Article  Google Scholar 

  6. 6.

    Zadeh LA (1965) Fuzzy sets. Inf Control 8:338–353

    MathSciNet  MATH  Article  Google Scholar 

  7. 7.

    Huang S-J, Chiu N-H (2009) Applying fuzzy neural network to estimate software development effort. Appl Intell 30:73–83. doi:10.1007/s10489-007-0097-4

    Article  Google Scholar 

  8. 8.

    Ishibuchi H, Nakashima T, Murata T (1999) Performance evaluation of fuzzy classifier systems for multidimensional pattern classification problems. IEEE Trans Syst Man Cybern, Part B, Cybern 29(5):601–618

    Article  Google Scholar 

  9. 9.

    Nozaki K, Ishibuchi H, Tanaka H (1996) Adaptive fuzzy rule-base classification systems. IEEE Trans Fuzzy Syst 4(3):238–250

    Article  Google Scholar 

  10. 10.

    Shi Y, Eberhart R, Chen Y (1989) Implementation of evolutionary fuzzy systems. IEEE Trans Fuzzy Syst 7(2):109–119

    Article  Google Scholar 

  11. 11.

    Young M (2002) The technical writers handbook. University Science, Mill Valley

    Google Scholar 

  12. 12.

    Jang J-SR (1993) ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans Syst Man Cybern 23:665–685. doi:10.1109/21.256541

    Article  Google Scholar 

  13. 13.

    Chang BR, Tsai H-F (2009) Quantum minimization for adapting ANFIS outputs to its nonlinear generalized autoregressive conditional heteroscedasticity. Appl Intell 31(1):31–46. doi:10.1007/s10489-007-0110-y

    MathSciNet  Article  Google Scholar 

  14. 14.

    Ubeyli ED (2009) Adaptive neuro-fuzzy inference systems for automatic detection of breast cancer. J Med Syst 33:353–358. doi:10.1007/s10916-008-9197-x

    Article  Google Scholar 

  15. 15.

    Ishibuchi H, Yamamoto T (2004) Fuzzy rule selection by multi-objective genetic local search algorithms and rule evaluation measures in data mining. Fuzzy Sets Syst 141:59–88

    MathSciNet  MATH  Article  Google Scholar 

  16. 16.

    Ishibuchi H, Nozaki K, Yamamoto N, Tanaka H (1995) Selecting fuzzy if-then rules for classification problems using genetic algorithms. IEEE Trans Fuzzy Syst 3(3):260–271

    Article  Google Scholar 

  17. 17.

    Roubos H, Setnes M (2001) Compact and transparent fuzzy models and classifiers through iterative complexity reduction. IEEE Trans Fuzzy Syst 9(4):516–524

    Article  Google Scholar 

  18. 18.

    Mohamadi H, Habibi J, Abadeh MS, Saadi H (2008) Data mining with a simulated annealing based fuzzy classification system. Pattern Recognit 41:1824–1833

    MATH  Article  Google Scholar 

  19. 19.

    Saniee AM, Habibi J, Soroush E (2008) Induction of fuzzy classification systems via evolutionary ACO-based algorithms. Int J Simul Syst Sci Technol 9(3):1–8

    Google Scholar 

  20. 20.

    Saniee AM, Habibi J, Lucas C (2007) Intrusion detection using a fuzzy genetics-based learning algorithm. J Netw Comput Appl 30:414–428

    Article  Google Scholar 

  21. 21.

    Saniee AM, Habibi J, Soroush E (2007) Induction of fuzzy classification systems using evolutionary ACO-based algorithms. In: Proceedings of the first Asia international conference on modelling and simulation (AMS’07). IEEE Press, New York

    Google Scholar 

  22. 22.

    Dorigo M, Stutzle T (2004) Ant colony optimization. MIT Press, Cambridge

    Google Scholar 

  23. 23.

    Dorigo M, Blum C (2005) Ant colony optimization theory: a survey. Theor Comput Sci 344:243–278

    MathSciNet  MATH  Article  Google Scholar 

  24. 24.

    Dorigo M, Maniezzo V, Colorni A (1996) The ant system: Optimization by a colony of cooperating agents. IEEE Trans Syst Man Cybern 26:1–13

    Google Scholar 

  25. 25.

    Jessica R, Dolores C, Javier C, Pedro I (2011) Using the ACO algorithm for path searches in social networks. Appl Intell. doi:10.1007/s10489-011-0304-1

    Google Scholar 

  26. 26.

    Blum C (2005) Review ant colony optimization: introduction and recent trends. Phys Life Rev 2:353–373

    Article  Google Scholar 

  27. 27.

    Parpinelli RS, Lopes HS, Freitas AA (2002) Data mining with an ant colony optimization algorithm. IEEE Trans Evol Comput 6:321–332

    Article  Google Scholar 

  28. 28.

    Liu B, Abbass HA, McKay B (2002) Density-based heuristic for rule discovery with ant-miner. In: The 6th Australia-Japan joint workshop on intelligent

    Google Scholar 

  29. 29.

    Liu B, Abbass HA, McKay B (2003) Classification rule discovery with ant colony optimization. In: Proc IEEE/WIC int conf on intell agent techno

    Google Scholar 

  30. 30.

    Martens D, De Backer M, Haesen R, Vanthienen J, Snoeck M, Baesens B (2007) Classification with ant colony optimization. IEEE Trans Evol Comput 11:651–656

    Article  Google Scholar 

  31. 31.

    Galea M, Shen Q (2006) Simultaneous ant colony optimization algorithms for learning linguistic fuzzy rules. In: Agraham A, Grosan C, Ramos V (eds) Swarm intelligence in data mining. Springer, Berlin, pp 75–99

    Google Scholar 

  32. 32.

    Mostafa FG, Mohamad SA (2010) Rule based classification system for medical data mining using fuzzy ant colony optimization. In: Proceedings of the world congress on engineering and computer science (WCECS 2010), vol 1, San Francisco, USA

    Google Scholar 

  33. 33.

    Abdul RB, Waseem S (2010) A correlation-based ant miner for classification rule discovery. Neural Comput Appl. doi:10.1007/s00521-010-0490-5

    Google Scholar 

  34. 34.

    Alatas B, Akin E (2005) FCACO: fuzzy classification rules mining algorithm with ant colony optimization. In: ICNC, vol 3, pp 787–797

    Google Scholar 

  35. 35.

    Kirkpatrick S, Gelatt CD Jr, Vecchi MP (1983) Optimization by simulated annealing. Science 220:671–680

    MathSciNet  MATH  Article  Google Scholar 

  36. 36.

    The uc irvine machine learning repository (2010) http://archive.ics.uci.edu/ml/. Accessed 8 June 2010

  37. 37.

    Ghazavi SN, Liao TW (2008) Medical data mining by fuzzy modeling with selected features. Artif Intell Med 43(3):195–206

    Article  Google Scholar 

  38. 38.

    Feyzullah T (2009) A comparative study on thyroid disease diagnosis using neural networks. Expert Syst Appl 36(1):944–949

    Article  Google Scholar 

  39. 39.

    Ali K, Ayturk K (2008) ESTDD: expert system for thyroid diseases diagnosis. Expert Syst Appl 34(1):242–246

    Article  Google Scholar 

  40. 40.

    Esin D, Akif D, Derya A (2011) An expert system based on generalized discriminant analysis and wavelet support vector machine for diagnosis of thyroid diseases. Expert Syst Appl 38(1):146–150

    Article  Google Scholar 

  41. 41.

    Luukka P, Leppalampi T (2006) Similarity classifier with generalized mean applied to medical data. Comput Biol Med 36(9):1026–1040

    Article  Google Scholar 

  42. 42.

    Ozbakir L, Baykasoglu A, Kulluk S (2008) Rule extraction from neural networks via ant colony algorithm for data mining applications. In: Maniezzo V et al (eds) Proceedings of the 2nd international conference on learning and intelligent optimization-LION 2007. Lecture notes in computer science, vol 5313. Springer, Berlin, pp 177–191

    Google Scholar 

  43. 43.

    Kahramanli H, Allahverdi N (2009) Rule extraction from trained adaptive neural networks using artificial immune systems. Expert Syst Appl 36:1513–1522

    Article  Google Scholar 

  44. 44.

    Yunyun W, Songcan C, Hui X (2011) Support vector machine incorporated with feature discrimination. Expert Syst Appl 38(10):12506–12513

    Article  Google Scholar 

  45. 45.

    Bach A (1990) Boltzmann’s probability distribution of 1877. Arch Hist Exact Sci 41:1–40. doi:10.1007/BF00348700

    MathSciNet  MATH  Google Scholar 

  46. 46.

    Metropolis N, Rosenbluth AW, Rosenbluth MN, Teller AH, Teller E (1953) Equations of state calculations by fast computating machines. J Chem Phys 21:1087–1091. doi:10.1063/1.1699114

    Article  Google Scholar 

  47. 47.

    Hanley JA, McNeil BJ (1983) A method of comparing the areas under receiver operating characteristic curves derived from the same cases. Radiology 148(3):839–843

    Google Scholar 

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Correspondence to Siriporn Supratid.

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Aribarg, T., Supratid, S. & Lursinsap, C. Optimizing the modified fuzzy ant-miner for efficient medical diagnosis. Appl Intell 37, 357–376 (2012). https://doi.org/10.1007/s10489-011-0332-x

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Keywords

  • Ant-miner
  • Fuzzy logic
  • Simulated annealing
  • Adaptive neuro-fuzzy inference system
  • Multi-class support vector machine