An application of a new meta-heuristic for optimizing the classification accuracy when analyzing some medical datasets

Expert Systems with Applications

Volumn 36, Issue 5, 2009, Pages 9240-9249

  Pham, Huy Nguyen Anh ^a   Triantaphyllou, Evangelos ^a  

^a Louisiana State University   (United States)

Author keywords

Classification errors; Genetic algorithms; HBA; Medical data mining; Optimization

Indexed keywords

ALGORITHMS; DATA MINING; ERRORS; HEURISTIC ALGORITHMS; HEURISTIC METHODS; INFORMATION MANAGEMENT; LEARNING SYSTEMS; MEDICAL APPLICATIONS; OPTIMIZATION;

CLASSIFICATION ACCURACIES; CLASSIFICATION ALGORITHMS; CLASSIFICATION ERRORS; COMPUTATIONAL CHALLENGES; COMPUTATIONAL RESULTS; DATA MINING COMMUNITIES; ERROR RATES; HBA; MACHINE-LEARNING; MEDICAL DATA MINING; MEDICAL DATA SETS; META-HEURISTIC; META-HEURISTIC APPROACHES; OPTIMIZATION PROBLEMS; PENALTY COSTS; PREDICTION ACCURACIES; STAND -ALONE; UNIVERSITY OF CALIFORNIA;

MEDICAL COMPUTING;

EID: 60849122524     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2008.12.007     Document Type: Article

References (35)

1. Abonyi J., and Szeifert H. Supervised fuzzy clustering for the identification of fuzzy classifiers. Pattern Recognition Letters 24 (2003) 2195-2207
2. Artificial Neural Network Toolbox 6.0 and Statistics Toolbox 6.0, Matlab Version 7.0. .
3. Asuncion, A., Newman, D. J. (2007). UCI-Machine Learning Repository. School of Information and Computer Sciences, University of California, Irvine, CA, USA.
4. Au, W. H., Chan, K. C. C. (2001). Classification with degree of membership: a fuzzy approach. In Proceedings of the 1st IEEE international conference on data mining pp. 35-42. San Jose, CA, USA.
5. Bennet, K. P., Blue, J. A. (1997). A support vector machine approach to decision trees,. Math Report, No. 97-100, Rensselaer Polytechnic Institute, Troy, NY, USA.
6. Blachnik M., and Duch W. Prototype-based threshold rules. LNCS of neural information processing Vol. 4234 (2006), Springer, Berlin/Heidelberg pp. 1028-1037
7. Cheung, N. (2001). Machine learning techniques for medical analysis. BSc thesis, School of Information Technology and Electrical Engineering, University of Queensland, Australia.
8. Çomaka E., Polatb K., Güneşb S., and Arslana A. A new medical decision making system: Least square support vector machine (LSSVM) with fuzzy weighting pre-processing. Expert Systems with Applications 32 2 (2007) 409-414 (February)
9. Domm, M., Engel, A., Louis, P. P., Goldberg, J. (2005). An integer support vector machine. In Proceedings of the 6th international conference on software engineering, artificial intelligence, networking and parallel/distributed computing pp. 144-149. Towson, MD, USA.
10. Duda R.O., and Hart P.E. Pattern classification and scene analysis (1973), Wiley Publisher, New York, NY, USA pp. 56-64
11. Fung, G., Mangasarian, O. L. (2001). Proximal support vector machine classifiers. In Proceedings of the 7th ACM SIGKDD international conference on knowledge discovery and data mining pp. 77-86. San Francisco, CA, USA: ACM Press.
12. Hamilton, H. J., Shan, N., Cercone, N. (1996). RIAC: A rule induction algorithm based on approximate classification. Technical Report, No. CS 96-06, University of Regina, Regina, Canada.
13. Jankowski, N., Kadirkamanathan, V. (1997). Statistical control of RBF-like networks for classification. In Proceedings of the 7th international conference on artificial neural networks (ICANN) pp. 385-390. Lausanne, Switzerland.
14. Kecman V., and Arthanari T. Comparisons of QP and LP based learning from empirical data. In: Monostori L., V́ancza J., and Ali M. (Eds). LNCS and LNAI (2001), Springer, New York, NY, USA 326-332 (June)
15. Lee, Y. J., Mangasarian, O. L. (2001). RSVM: Reduced support vector machines. In Proceedings of the first SIAM international conference on data mining, Chicago, IL, USA.
16. Lee Y.J., and Mangasarian O.L. SSVM: A smooth support vector machine for classification. Computational Optimization and Applications 20 1 (2001) 5-22
17. Leon, W. D., IV., (2006). Enhancing pattern classification with relational fuzzy neural networks and square BK-products. PhD dissertation in computer science. FL, USA: Springer (pp. 71-74).
18. Michie D., Spiegelhalter D.J., and Taylor C.C. Machine learning neural and statistical classification. Series artificial intelligence (1994), Prentice Hall, Englewood Cliffs, NJ, USA 157-160
19. Nakashima, T., Nakai, G., Ishibuchi, H. (2003). Constructing fuzzy ensembles for pattern classification problems. In Proceedings of the international conference on systems, man and cybernetics (Vol. 4, pp. 3200-3205). Washington, DC, USA (October).
20. Nauck D., and Kruse R. Obtaining interpretable fuzzy classification rules from medical data. Artificial Intelligence in Medicine 16 (1999) 149-169
21. Pena-Reyes C.A., and Sipper M. A fuzzy-genetic approach to breast cancer diagnosis. Artificial Intelligence in Medicine 17 (1999) 131-155
22. Pham, D. T., Dimov, S. S., Salem, Z. (2000). Technique for selecting examples in inductive learning. In Proceedings of the European symposium on intelligent techniques (ESIT 2000) pp. 119-127. Aachen, Germany.
23. Pham H.N.A., and Triantaphyllou E. The impact of overfitting and overgeneralization on the classification accuracy in data mining. In: Maimon O., and Rokach L. (Eds). Soft computing for knowledge discovery and data mining, part 4 (2007), Springer, New York, NY, USA 391-431
24. Pham H.N.A., and Triantaphyllou E. Prediction of diabetes by employing a new data mining approach which balances fitting and generalization. In: Lee R.Y. (Ed). Studies in computation intelligence Vol. 131 (2008), Springer, Germany 11-26
25. Polat K.A., Sahan S., Kodaz H., and Gunes S. Breast cancer and liver disorders classification using artificial immune recognition system (AIRS) with performance evaluation by fuzzy resource allocation mechanism. Expert Systems with Applications 32 (2007) 172-183
26. Quinlan J.R. Improved use of continuous attributes in C4.5. Artificial Intelligence Research 4 (1996) 77-90
27. Rutkowski L., and Cpalka K. Flexible neuro-fuzzy systems. IEEE Transactions on Neural Networks 14 (2003) 554-574
28. Setiono R. Generating concise and accurate classification rules for breast cancer diagnosis. Artificial Intelligence in Medicine 18 (2000) 205-219
29. Shevked, Z., Dakovski, L. (2007). Learning and classification with prime implicants applied to medical data diagnosis. In Proceedings of the 2007 international conference on computer systems and technologies, Rousse, Bulgaria, June.
30. Smith, J. W., Everhart, J. E., Dickson, W. C., Knowler, W. C., Johannes, R. S. (1998). Using the ADAP learning algorithm to forecast the onset of diabetes mellitus. In Proceedings of the 12th symposium on computer applications and medical care pp. 261-265. Los Angeles, CA, USA.
31. Ster, B., Dobnikar, A. (1996). Neural networks in medical diagnosis comparison with other methods. In Proceedings of the international conference on engineering applications of neural networks (EANN'96) pp. 427-430. London, UK.
32. Thomas J.W., and Hofer J.P. Accuracy of risk-adjusted mortality rate as a measure of hospital quality of care. Medical Care 37 1 (1999) 83-92
33. Turner M.G. Landscape ecology: The effects of pattern on process. Annual Review of Ecology and Systematics 20 (1989) 171-197
34. Van G.T., Suykens J.A.K., Lanckriet G., Lambrechts A., de Moor B., and Vandewalle J. Bayesian framework for least squares support vector machine classifiers Gaussian processes and Kernel Fisher discriminant analysis. Neural Computation 14 (2002) 1115-1147
35. Weiss, S. M., Kapouleas, I. (1989). An empirical comparison of pattern recognition, neural nets, and machine learning classification methods. In Proceedings of the 11th international joint conference on artificial intelligence pp. 781-787. Detroit, MI, USA.