Advertisement

Artificial Intelligence Review

, Volume 45, Issue 4, pp 471–488 | Cite as

Estimation of automatic detection of erythemato-squamous diseases through AdaBoost and its hybrid classifiers

  • N. Badrinath
  • G. Gopinath
  • K. S. RavichandranEmail author
  • R. Girish Soundhar
Article

Abstract

This paper focuses on efficient techniques based on AdaBoost and its hybrid (AdaBoost-SVM) classifiers for an automatic detection of the erythemato-squamous diseases (ESD). The classification of ESD requires an enormous amount of computational effort, as all the features of the diseases have more than 90 % commonality. Some of the approaches reported in literature are fuzzy logic, artificial neural networks, neuro-fuzzy models and support vector machine. Recently, AdaBoost and its hybrid algorithms are widely used to enhance the accuracy of the learning algorithms. In this paper, we propose AdaBoost and its hybrid algorithms for diagnosis of erythemato-squamous diseases for the first time. User-friendly-interface is designed to assist dermatologists to estimate the ESD with the help of 34 features includes patient’s histopathological and clinical data sets. Efficient interface design is based on the following steps: (1) feature selection for all the 34 parameters involved in the erythemato-squamous disease is done using association rule based Apriori algorithm, (2) association rules are used to find \(\upalpha \%\) of transactions (diseases) to meet \(\upbeta \%\) of the features of the diseases, (3) support value of all the subset of \(\upalpha \) transaction from \(\upalpha \%\) of transactions is found and the dominant subset is found using Apriori algorithm, and (4) AdaBoost and its hybrid classifiers are used to classify the diseases. In this paper, the real, modest, gentle and hybrid AdaBoost algorithms are implemented and tested with the dataset of 366 patients. The data preprocessing reduces the dimensionality of the dataset and improves the time complexity of the estimation of the diseases. Proposed methodologies are proved as a potential solution for diagnosing ESD with significant improvement in computational time and accuracy compared to other models discussed in the recent literature.

Keywords

Erythemato-squamous diseases AdaBoost (real, gentle and modest) Support vector machine (SVM) Feature selection  Apriori algorithm 

References

  1. Agrawal R, Shafer JC (1996) Parallel mining of association rules. IEEE Trans Knowl Data Eng 8(6):962–969CrossRefGoogle Scholar
  2. Agrawal R, Srikant R (1994) Fast algorithms for mining association rules in large data bases. In: Proceedings of the 20th international conference on very large data bases, September 12–15, pp 487–499Google Scholar
  3. Aruna S, Nandakishore LV, Rajagopalan SP (2012) A hybrid feature selection method based on IGSBFS and naïve bayes for the diagnosis of erythemato-squamous diseases. Int J Comput Appl 41(7):13–18Google Scholar
  4. Balas VE, Fodor J, Várkonyi-Kóczy AR, Dombi J, Lakhmi CJ (2013) Soft computing applications. In: Proceedings of the 5th international workshop soft computing applications (SOFA) 195:01–04Google Scholar
  5. Bauer E, Kohavi R (1999) An empirical comparison of voting classification algorithms: bagging, boosting, and variants. Mach Learn 36:105–139CrossRefGoogle Scholar
  6. Breiman L (1996) Bagging predictors. Mach Learn 24(2):123–140MathSciNetzbMATHGoogle Scholar
  7. Castellano G, Castiello C, Fanelli AM, Leone C (2003) Diagnosis of dermatological diseases by a neuro-fuzzy approach. In: Proceedings of international conference in fuzzy logic and technology (EUSFLAT 2003). Zittau, Germany, September 10–12. 747–750Google Scholar
  8. Chatterjee C, Roychowdhury VP, Chong EKP (1998) On relative convergence properties of principal component analysis algorithms. IEEE Trans Neural Netw 9:319–329CrossRefGoogle Scholar
  9. Cover T, Hart P (1967) Nearest neighbor pattern classification. Proc IEEE Trans Inf Theory 13(1):21–27CrossRefzbMATHGoogle Scholar
  10. Davar G, Salimi H, Bitaraf AA, Khademian Y (2011) Detection of erythemato-squamous diseases using AR-CatfishBPSO-KSVM. Signal Image Process Int J (SIPIJ) 2(4):57–72CrossRefGoogle Scholar
  11. Demuth H, Beale M (1992) Neural network toolbox, for use with MATLAB. User’s Guide 4:18–20Google Scholar
  12. Duda R, Hart P, Stork D (2001) Pattern classification, 2nd edn. John Wiley and Sons, New YorkzbMATHGoogle Scholar
  13. Efron B (1982) The jackknife, the bootstrap and other resampling plans. Society for Industrial and Applied Mathematics (SIAM), PhiladelphiaCrossRefzbMATHGoogle Scholar
  14. Elsayad AM (2010) Diagnosis of erythemato-squamous diseases using ensemble of data mining methods. ICGST-BIME J 10(1):13–23Google Scholar
  15. Freund Y, Schapire R (1996a) Experiments with a new boosting algorithm. In: Thirteenth international conference on machine learning. Bari, Italy, pp 148–156Google Scholar
  16. Freund Y, Schapire R (1996b) Game theory on-line prediction and boosting. In: Ninth annual conference on computer learning theory. Desenzano del Garda, Italy, pp 325–332Google Scholar
  17. Friedman J, Hastie T, Tibshirani R (2000) Additive logistic regression: a statistical view of boosting. Ann Stat 28(2):337–374MathSciNetCrossRefzbMATHGoogle Scholar
  18. Gómez-Verdejo V, Ortega-Moral M, Arenas-Gárcia J, Figueiras-Vidal A (2006) Boosting of weighting critical and erroneous samples. Neurocomputing 69(7–9):679–685CrossRefGoogle Scholar
  19. Güvenir HA, Emeksiz N (2000) An expert system for the differential diagnosis of erythemato-squamous diseases. Exp Syst Appl 18:43–49CrossRefGoogle Scholar
  20. Guvenir HA, Demiroz G, Ilter N (1998) Learning differential diagnosis of erythemato-squamous diseases using voting feature intervals. Artif Intell Med 13:147–165CrossRefGoogle Scholar
  21. Hastie T, Tibshirani R, Friedman J (2001) The elements of statistical learning, 2nd edn. Springer, BerlinCrossRefzbMATHGoogle Scholar
  22. Hyvärinen A (1999) Fast and robust fixed-point algorithms for independent component analysis. IEEE Trans Neural Netw 10(3):626–634CrossRefGoogle Scholar
  23. Hyvärinen A, Oja E (2000) Independent component analysis: algorithms and applications. IEEE Trans Neural Netw 13:411–430CrossRefGoogle Scholar
  24. Jang JSR (1993) ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans Syst Man Cybern 23(3):665–685CrossRefGoogle Scholar
  25. Jang JSR (1992) Self-learning fuzzy controllers based on temporal backpropagation. IEEE Trans Neural Netw 3(5):714–723CrossRefGoogle Scholar
  26. Nanni Loris (2006) An ensemble of classifiers for the diagnosis of erythemato-squamous diseases. Neurocomputing 69:842–845CrossRefGoogle Scholar
  27. Narendra PM, Fukunaga K (1977) A branch and bound algorithm for feature subset selection. IEEE Trans Comput 26(9):917–922. doi: 10.1109/TC.1977.1674939 CrossRefzbMATHGoogle Scholar
  28. Parthiban L, Subramainan R (2009) An intelligent agent for detection of erythemato-squamous diseases using co-active neuro-fuzzy inference system and genetic algorithm. In: Proceeding of the international conference on intelligent agent and multi-agent systems, 01–06Google Scholar
  29. Ravichandran KS, Alsheyuhi SS (2011) FELM based intelligent optimal switching capacitor placement, FSKD, pp 366-371Google Scholar
  30. Ravichandran KS, Badrinath N, Gopinath G, Ravalli S, Sindhura J (2014) An efficient approach to an automatic detection of erythemato-squamous diseases. Neural Comput Appl 25(1):105–114CrossRefGoogle Scholar
  31. Saltelli A et al (2008) Global sensitivity analysis: the premier. Wiley, LondonzbMATHGoogle Scholar
  32. Schapire R, Singer Y (1999) Improved boosting algorithms using confidence-rated predictions. Mach Learn 37(3):297–336CrossRefzbMATHGoogle Scholar
  33. Übeyli ED, Güler I (2005) Automatic detection of erythemato-squamous diseases using adaptive neuro-fuzzy inference systems. Comput Biol Med Int J 35:421–433Google Scholar
  34. Ubeyli ED, Dogdu E (2010) Automatic detection of erythemato-squamous diseases using k-means clustering. J Med Syst 34:179–184CrossRefGoogle Scholar
  35. Ubeyli ED (2008) Multiclass support vector machines for diagnosis of erythemato-squamous diseases. Exp Syst Appl 35:1733–1740CrossRefGoogle Scholar
  36. Ubeyli ED (2009) Combined neural networks for diagnosis of erythemato-squamous diseases. Exp Syst Appl 36(5107–5112):2009Google Scholar
  37. Vapnik VN (1995) The nature of statistical learning theory. Springer, New YorkCrossRefzbMATHGoogle Scholar
  38. Vezhnevets A, Vezhnevets V (2005) Modest adaboost—teaching adaboost to generalize better. Graphicon 12(5):987–997Google Scholar
  39. Wang J, Gao L, Zhang H, Xu J (2011) Adaboost with SVM-based classifier for the classification of brain motor imagery tasks. Lecture Notes in Computer Science. 6766:629–634Google Scholar
  40. Xie J, Lei J, Xie W, Gao X, Shi Y, Liu X (2012) Novel hybrid feature selection algorithms for diagnosing erythemato-squamous diseases, health information science. Lecture Notes in Computer Science 7231:173–185Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2015

Authors and Affiliations

  • N. Badrinath
    • 1
  • G. Gopinath
    • 1
  • K. S. Ravichandran
    • 2
    Email author
  • R. Girish Soundhar
    • 3
  1. 1.Department of Computer Science and EngineeringBharathidasan UniversityTiruchirapalliIndia
  2. 2.School of ComputingSASTRA UniversityThanjavurIndia
  3. 3.Sri Manakula Vinayagar Medical College and HospitalKalitheerthalkuppam, Madagadipet, PuducherryIndia

Personalised recommendations