Linguistic hedges fuzzy feature selection for differential diagnosis of erythemato-squamous diseases

Advances in Intelligent Systems and Computing

Volumn 195 AISC, Issue , 2013, Pages 487-500

  Azar, Ahmad Taher ^a   El Said, Shaimaa A ^b   Balas, Valentina Emilia ^c   Olariu, Teodora ^d  

^a MISR UNIVERSITY FOR SCIENCE AND TECHNOLOGY   (Egypt)

^b Zagazig University ^*  (Egypt)

^c AUREL VLAICU UNIVERSITY OF ARAD   (Romania)

^d VASILE GOLDIS WESTERN UNIVERSITY OF ARAD   (Romania)

Author keywords

Erythemato Squamous Diseases; Feature selection (FS); Linguistic Hedge (LH); Soft Computing; Takagi Sugeno Kang (TSK) fuzzy inference system

Indexed keywords

CLASSIFICATION (OF INFORMATION); COMPUTER AIDED DIAGNOSIS; FEATURE EXTRACTION; FUZZY RULES; LINGUISTICS; SOFT COMPUTING; TESTING; WOODEN FENCES;

AUTOMATIC DIAGNOSTIC SYSTEMS; CLASSIFICATION ACCURACY; DIFFERENTIAL DIAGNOSIS; ERYTHEMATO-SQUAMOUS DISEASE; FUZZY INFERENCE SYSTEMS; LINGUISTIC HEDGES; NEURAL FUZZY CLASSIFIER; PARTITION MODEL;

FUZZY INFERENCE;

EID: 84872848288     PISSN: 21945357     EISSN: None     Source Type: Book Series    
DOI: 10.1007/978-3-642-33941-7_43     Document Type: Conference Paper

References (37)

1. Abdi, M.J., Giveki, D.: Automatic detection of erythemato-squamous diseases using PSO-SVM based on association rules. Eng. Appl. Artif. Intel. (2012), doi:10.1016/j.engappai.2012.01.017
2. Barati, E., Saraee, M., Mohammadi, A., et al.: A Survey on Utilization of Data Mining Approaches for Dermatological (Skin) Diseases Prediction. Cyber Journals: Multidisciplinary Journals in Science and Technology, Journal of Selected Areas in Health Informatics (JSHI): March Edition, 1-11 (2011)
3. Cetili, B.: Development of an adaptive neuro-fuzzy classifier using linguistic hedges: Part 1. Expert Systems with Applications 37(8), 6093-6101 (2010a)
4. Cetili, B.: The effect of linguistic hedges on feature selection: Part 2. Expert Systems with Applications 37(8), 6102-6108 (2010b)
5. Chrysostomou, K.A.: The Role of Classifiers in Feature selection Selection: Number vs Nature, PhD dissertation, School of Information Systems. Computing and mathematics, Brunel University (2008)
6. Diamantidis, N.A., Karlis, D., Giakoumakis, E.A.: Unsupervised stratification of crossvalidation for accuracy estimation. Artif. Intell. 116(1-2), 1-16 (2000) (Pubitemid 30560294)
7. Francois, D., Rossi, F., Wertz, V.: Verleysen M Resampling methods for parameter-free and robust feature selection with mutual information. Neurocomputing 70, 1276-1288 (2007) (Pubitemid 46349971)
8. Gagliardi, F.: Instance-based classifiers applied to medical databases: diagnosis and knowledge extraction. Artif. Intell. Med. 52(3), 123-139 (2011)
9. Govenir, H.A., Emeksiz, N.: An expert system for the differential diagnosis of erythemato-squamous diseases. Expert Syst. Appl. 18(1), 43-49 (2000)
10. Güvenir, H.A., Demiroz, G., Ilter, N.: Learning differential diagnosis of erythematosquamous diseases using voting feature intervals. Artif. Intell. Med. 13(3), 147-165 (1998) (Pubitemid 28341691)
11. Guyon, I., Steve, G., Nikravesh, M., Zadeh, L.A.: Feature Extraction: Foundations and Applications. Springer, Heidelberg (2006)
12. Jang, J.S.R.: ANFIS: Adaptive-Network-Based Fuzzy Inference System. IEEE Transactions on Systems, Man, and Cybernetics 23(3), 665-685 (1993)
13. Jang, J.S.R., Sun, C.T.: Neuro-fuzzy modeling and control. Proceedings of the IEEE 83(3), 378-406 (1995)
14. Jang, J.S.R., Sun, C.T., Mizutani, E.: Neuro-Fuzzy and soft computing. Prentice-Hall, Englewood Cliffs (1997)
15. Kabari, L.G., Bakpo, F.S.: Diagnosing skin diseases using an artificial neural network. In: Proceeding of 2nd International Conference on Adaptive Science & Technology (ICAST 2009), pp. 187-191. IEEE Press, New York (2009)
16. Karabatak, M., Ince, M.C.: A new feature selection method based on association rules for diagnosis of erythemato-squamous diseases. Expert Syst. Appl. 36(10), 12500-12505 (2009)
17. Lekkas, S., Mikhailov, L.: Evolving fuzzy medical diagnosis of Pima Indians diabetes and of dermatological diseases. Artif. Intell. Med. 50(2), 117-126 (2010)
18. Luukka, P.: Feature selection using fuzzy entropy measures with similarity classifier. Expert Syst. Appl. 38(4), 4600-4607 (2011a)
19. Luukka, P.: A New Nonlinear Fuzzy Robust PCA Algorithm and Similarity Classifier in Classification of Medical Data Sets. International Journal of Fuzzy Systems 13(3), 153-162 (2011b)
20. Luukka, P., Leppälampi, T.: Similarity classifier with generalized mean applied to medical data. Comput. Biol. Med. 36(9), 1026-1040 (2006) (Pubitemid 44118242)
21. Moller, M.F.: A scaled conjugate gradient algorithm for fast supervised learning. Neural Networks 6, 525-533 (1993)
22. Nanni, L.: An ensemble of classifiers for the diagnosis of erythemato-squamous diseases. Neurocomputing 69(7-9), 842-845 (2006) (Pubitemid 43230387)
23. Ozcift, A., Gulten, A.: A Robust Multi-Class Feature Selection Strategy Based on Rotation Forest Ensemble Algorithm for Diagnosis of Erythemato-Squamous Diseases. J. Med. Syst. 36(2), 941-949 (2012)
24. Parthiban, L., Subramanian, R.: An intelligent agent for detection of erythemato-squamous diseases using Co-active Neuro-Fuzzy Inference System and genetic algorithm. In: International Conference on Intelligent Agent & Multi-Agent Systems, IAMA, pp. 1-6. IEEE Press, New York (2009)
25. Polat, K., Gunes, S.: A novel hybrid intelligent method based on C4.5 decision tree classifier and one-against-all approach for multi-class classification problems. Expert Systems with Applications 36(2), 1587-1592 (2009)
26. Polat, K., Günes, S.: The effect to diagnostic accuracy of decision tree classifier of fuzzy and k-NN based weighted pre-processing methods to diagnosis of erythema-to-squamous diseases. Digital Signal Processing 16(6), 922-930 (2006) (Pubitemid 44779329)
27. Revett, K., Gorunescu, F., Salem, A.B., El-Dahshan, E.S.: Evaluation of the feature space of an erythematosquamous dataset using rough sets. Ann. Univers. Craiova, Romania, Math. Comput. Sci. Ser. 36(2), 123-130 (2009)
28. Salzberg, S.L.: On comparing classifiers: Pitfalls to avoid and a recommended approach. Data Mining and Knowledge Discovery 1, 317-327 (1997)
29. Sun, C.T., Jang, J.S.R.: A neuro-fuzzy classifier and its application. In: Proc. of IEEE Int. Conf. on Fuzzy Systems, vol. 1, pp. 94-98. IEEE Press, San Francisco (1993)
30. Takagi, T., Sugeno, M.: Fuzzy identification of systems and its applications to modeling and control. IEEE Trans. Syst. Man. Cybern. 15(1), 116-132 (1985)
31. Xie, J., Lei, J., Xie, W., Gao, X., Shi, Y., Liu, X.: Novel Hybrid Feature Selection Algorithms for Diagnosing Erythemato-Squamous Diseases. In: He, J., Liu, X., Krupinski, E.A., Xu, G. (eds.) HIS 2012. LNCS, vol. 7231, pp. 173-185. Springer, Heidelberg (2012)
32. Xie, J., Wang, C.: Using support vector machines with a novel hybrid feature selection method for diagnosis of erythemato-squamous diseases. Expert Syst. Appl. 38(5), 5809-5815 (2011)
33. Xie, J.Y., Xie, W.X., Wang, C.X., et al.: A novel hybrid feature selection method based on IFSFFS and SVM for the diagnosis of erythemato-squamous diseases. In: JMLR Workshop and Conference Proceedings. Workshop on Applications of Pattern Analysis, vol. 11, pp. 142-151. MIT Press, Windsor (2010)
34. UCI, Machine Learning Repository, http://archive.ics.uci.edu/ml/index. html (accessed on July 12, 2012)
35. Übeyli, E.D.: Combined neural networks for diagnosis of erythemato-squamous diseases. Expert Syst. Appl. 36(3), 5107-5112 (2009)
36. Ubeyli, E.D.: Multiclass support vector machines for diagnosis of erythemato-squamous diseases. Expert Syst. Appl. 35(4), 1733-1740 (2008)
37. Ubeyli, E.D., Güler, I.: Automatic detection of erythemato-squamous diseases using adaptive neuro-fuzzy inference systems. Computers in Biology and Medicine 35(5), 147-165 (2005)