Artificial Intelligence in Medicine
Volume 43, Issue 3 , Pages 195-206 , July 2008

Medical data mining by fuzzy modeling with selected features

Received 26 January 2008 ,Revised 17 April 2008 ,Accepted 20 April 2008.

References 

  1. Guillaume S. Designing fuzzy inference systems from data: an interpretability-oriented review. IEEE Transactions on Fuzzy Systems. 2001;9:426–443
  2. Liao TW. Mining human interpretable knowledge using automatic data-driven fuzzy modeling methods—a review. In:  Triantaphyllou E,  Felici G editor. Data mining and knowledge discovery approaches based on rule induction techniques, massive computing series. New York: Springer; 2006;p. 495–550
  3. Tsai CY, Chiu CC. A case-based reasoning system for PCB principal process parameter identification. Expert Systems with Application. 2007;32:1183–1193
  4. Haindl M, Somol P, Ververidis D, Kotropoulos C. Feature selection based on mutual correlation. In:  Martínez-Trinidad JF, et al. editor. Lecture notes in computer science. vol. 4225:Heidelberg/Berlin: Springer; 2006;p. 569–577
  5. Park JS, Shazzad KM, Kim DS. Toward modeling lightweight intrusion detection system through correlation-based hybrid feature selection. In:  Feng D,  Lin D,  Yung M editor. Lecture notes in computer science. vol. 3822:Heidelberg/Berlin: Springer; 2005;p. 279–289
  6. Loo LH, Roberts S, Hrebien L, Kam M. New criteria for selecting differentially expressed genes. IEEE Engineering in Medicine and Biology Magazine. 2007;17–26
  7. Inza I, Larrañaga P, Blanco R, Cerrolaza AJ. Filter versus wrapper gene selection approaches in DNA microarray domains. Artificial Intelligence in Medicine. 2004;31:91–103
  8. Yen GG, Lin KC. Wavelet packet feature extraction for vibration monitoring. IEEE Transactions on Industrial Electronics. 2000;47(3):650–667
  9. Bijlani R, Cheng Y, Pearce DA, Brooks AI, Ogihara M. Prediction of biologically significant components from microarray data: independently consistent expression discriminator. Bioinformatics. 2003;19(1):62–70
  10. Kira K, Rendell LA. A practical approach to feature selection. In:  Sleeman D,  Edwards P editor. ICML-92: Proceedings of the ninth international conference on machine learning. Aberdeen, Scotland: Morgan Kaufmann, CA. 1992;p. 129–134
  11. Liao TW, Celmins AK, Hammell RJ. A fuzzy c-means variant for the generation of fuzzy term sets. Fuzzy Sets and Systems. 2003;135:241–257
  12. Liao TW. Fuzzy reasoning based automatic inspection of radiographic welds: weld recognition. Journal of Intelligent Manufacturing. 2004;15:69–85
  13. Jang JSR. ANFIS—adaptive-networ k-based fuzzy inference system. IEEE Transactions on Systems Man and Cybernetics. 1993;23:665–685
  14. Keller JM, Gray MR, Givens JA. A fuzzy k-nearest neighbor algorithm. IEEE Transactions on Systems Man and Cybernetics. 1985;15:580–585
  15. Cios KJ, Moore GW. Uniqueness of medical data mining. Artificial Intelligence in Medicine. 2002;26:1–24
  16. Liao TW Classification of weld flaws with imbalanced class data. Expert Systems with Application 2008, doi:10.1016/j.eswa.2007.08.044.
  17. Kurgan LA, Musilek P. A survey of knowledge discovery and data mining process models. The Knowledge Engineering Review. 2006;21(1):1–24
  18. Belacel N, Boulassel MR. Multicriteria fuzzy assignment method: a useful tool to assist medical diagnosis. Artificial Intelligence in Medicine. 2001;21:201–207
  19. Belacel N, Raval HB, Punnen AP. Learning multicriteria fuzzy classification method PROAFTN from data. Computers & Operations Research. 2007;34:1885–1898
  20. Seker H, Odetayo MO, Petrovic D, Naguib RN. A fuzzy logic based-method for prognostic decision making in breast and prostate cancers. IEEE Transactions on Information Technology in Biomedicine. 2003;7(2):114–122
  21. Ruiz-Gomez J, Lopez-Baldan MJ, Garcia-Cerezo A. Input-output fuzzy identification of nonlinear multivariate systems, application to a case of AIDS spread forecast. In:  Mira J editors. Lecture notes in computer science. vol. 2687:Heidelberg/Berlin: Springer; 2003;p. 481–488
  22. Aruna P, Puviarasan N, Palaniappan B. An investigation of neuro-fuzzy systems in psychosomatic disorders. Expert Systems with Applications. 2005;28:673–679
  23. Tsipouras MG, Voglis C, Fotiadis DI. A framework for fuzzy expert system creation—application to cardiovascular diseases. IEEE Transactions on Biomedical Engineering. 2007;54(10):2089–2105
  24. Kim MW, Ryu JW. Optimized fuzzy classification using genetic algorithm. In:  Wang L,  Jin Y editor. Lecture notes in artificial intelligence. vol. 3613:Berlin/Heidelberg: Springer; 2005;p. 392–401
  25. Chiang IJ, Shieh MJ, Hsu JYJ, Wong JM. Building a medical decision support system for polyp screening by using fuzzy classification trees. Applied Intelligence. 2005;22:61–75
  26. Gonçalves LB, Vellasco MBR, Pacheco MAC, de Souza FJ. Inverted hierarchical neuro-fuzzy BSP system: a novel neuro-fuzzy model for pattern classification and rule extraction in databases.. IEEE Transactions on Systems, Man, and Cybernetics-Part C: Applications and Reviews. 2006;36(2):236–248
  27. Nauck D, Kruse R. Obtaining interpretable fuzzy classification rules from medical data. Artificial Intelligence in Medicine. 1999;16:149–169
  28. Keles A, Hasiloglu AS, Keles A, Aksoy Y. Neuro-fuzzy classification of prostate cancer using NEFCLASS-J. Computers in Biology and Medicine. 2007;37:1617–1628
  29. Ravi V, Zimmermann HJ. Fuzzy rule based classification with FeatureSelector and modified threshold accepting. European Journal of Operational Research. 2000;123:16–28
  30. Strackeljan J, Behr D, Detro F. FeatureSelector: a plug in for feature selection with DataEngine. In: Proceedings of the first international data analysis symposium. Aachen, Germany. 1997;
  31. Ravi V, Reddy PJ, Zimmermann HJ. Pattern classification with principal component analysis and fuzzy rule bases. European Journal of Operational Research. 2000;126:526–533
  32. Lee HM, Chen CM, Chen JM, Jou YL. An efficient fuzzy classifier with feature selection based on fuzzy entropy. IEEE Transactions on Systems, Man, Cybernetics. 2001;31(3):426–432
  33. Abonyi J, Szefert F. Supervised fuzzy clustering for the identification of fuzzy classifiers. Pattern Recognition Letters. 2003;24:2195–2207
  34. Xu W, Xia S, Xie H. Application of CMAC-based networks on medical image classification. In:  Yin F,  Wang J,  Guo C editor. Lecture notes in computer science. vol. 3173:Heidelberg/Berlin: Springer; 2004;p. 953–958
  35. Song H, Lee S, Kim D, Park GT. New methodology of computer aided diagnostic system on breast cancer. In:  Wang J,  Liao X,  Yi Z editor. Lecture notes in computer science. vol. 3498:Heidelberg/Berlin: Springer; 2005;p. 780–789
  36. Polat K, Güneş S. An expert system approach based on principal component analysis and adaptive neuro-fuzzy inference system to diagnosis of diabetes disease. Digital Signal Processing. 2007;17:702–710
  37. Luukka P, Leppälampi T. Similarity classifier with generalized mean applied to medical data. Computers in Biology and Medicine. 2006;36:1026–1040
  38. Kukkurainen P, Turunen E. Many-valued similarity reasoning: an axiomatic approach. Multiple-valued Logic. 2002;8:751–760
  39. Luukka P. Similarity classifier using similarity measure derived from Yu's norms in classification of medical data sets. Computers in Biology and Medicine. 2007;37:1133–1140
  40. http://archive.ics.uci.edu/ml/datasets.html
  41. Liao TW, Li DM, Li YM. Detection of welding flaws from radiographic images with fuzzy clustering methods. Fuzzy Sets and Systems. 1999;108:145–158

PII: S0933-3657(08)00052-3

doi: 10.1016/j.artmed.2008.04.004

Artificial Intelligence in Medicine
Volume 43, Issue 3 , Pages 195-206 , July 2008