Enhancing the performance of libSVM classifier by kernel f-score feature selection

Communications in Computer and Information Science

Volumn 40, Issue , 2009, Pages 533-543

  Sarojini, Balakrishnan ^a,b   Ramaraj, Narayanasamy ^c   Nickolas, Savarimuthu ^d  

^a MOTHER TERESA WOMEN S UNIVERSITY   (India)

^b K L N COLLEGE OF INFORMATION TECHNOLOGY   (India)

^c GKM COLLEGE OF ENGINEERING AND TECHNOLOGY   (India)

^d NATIONAL INSTITUTE OF TECHNOLOGY   (India)

Author keywords

False negative; False positive; Feature selection; LibSVM classifier; Medical data mining; Predictive accuracy

Indexed keywords

FALSE NEGATIVES; FALSE POSITIVE; FEATURE SELECTION; LIBSVM CLASSIFIER; MEDICAL DATA MINING; PREDICTIVE ACCURACY;

CLASSIFICATION (OF INFORMATION); CLASSIFIERS; DATA MINING; DATA PROCESSING; DIAGNOSIS; MEDICAL COMPUTING;

FEATURE EXTRACTION;

EID: 70349909349     PISSN: 18650929     EISSN: None     Source Type: Book Series    
DOI: 10.1007/978-3-642-03547-0_51     Document Type: Conference Paper

References (48)

1. Burke, H.B., Goodman, P.H., Rosen, D.B., Henson, D.E., Weinstein, J.N., Harrell Jr., F.E., Marks, J.R., Winchester, D.P., Bostwick, D.G.: Artificial neural networks improve the accuracy of cancer survival prediction. Cancer 79, 857-862 (1997)
2. Lavrac, N.: Selected techniques for data mining in medicine. Artif. Intell. Med. 16, 3-23 (1999)
3. Cios, K.J., Moore, G.: Uniqueness of medical data mining. Artif. Intell. Med. 26, 1-24 (2002)
4. Liu, Motoda, H.: Feature Extraction, Construction and Selection. In: A Data Mining Perspective. Kluwer Academic Publishers, Boston (1998); 2nd Printing (2001)
5. Split, A.M.T., Stegwee, R.A., Teitink, J.A.C.: Business intelligent for healthcare organizations. In: Proceeding of the 35th Annual Hawaii International Conference on System Sciences. IEEE Press, New York (2002)
6. Abraham, R., Simha, J.B., Iyengar, S.: Medical datamining with a new algorithm for feature selection and Naïve Bayesian classifier. In: 10th International Conference on Information Technology
7. Chang, C.-C., Lin, C.-J.: LIBSVM a library for support vector machines (2005), http://www.csie.ntu.edu.tw/cjlin/libsvm
8. Blake, C.L., Merz, C.J.: UCI repository of machine learning databases (1998), http://www.ics.uci.edu/mlearn/MLRepository.html
9. Chen, Y.-W., Lin, C.-J.: Combining SVMs with various feature selection strategies (2005), http://www.csie.ntu.edu.tw/cjlin/papers/features.pdf
10. Bradley, P.S., Mangasarian, O.L.: Feature selection via concave minimization and support vector machines. In: Proceedings of the 13th international conference on machine learning, San Francisco, CA, pp. 82-90 (1998)
11. Weston, J., Mukherjee, S., Chapelle, O., Pontil, M., Poggio, T., Vapnik, V.N.: Feature selection for SVMs. In: Leen, T., Dietterich, T., Tresp, V. (eds.) Advances in Neural Information Processing Systems 13, pp. 668-674 (2001)
12. Guyon, I., Weston, J., Barnhill, S., Bapnik, V.: Gene selection for cancer classification using support vector machines. Machine Learning 46(1-3), 389-422 (2002)
13. Duan, K., Keerthi, S.S., Poo, A.N.: Evaluation of simple performance measures for tuning SVM hyperparameters. Neurocomputing 51, 41-59 (2003)
14. Computer aided diagnosis, data mining combine for improved care, Health care IT (2006)
15. Predicting Health: Jeff Kaplan, managing director at Apollo Data Technologies LLC in Chicago
16. Roshawnna Scales, Mark Embrechts: Computational intelligence techniques for medical diagnostics
17. Kononeko, I., Kukar, M.: Machine learning for medical diagnosis. In: Workshop on Computer- Aided Data Analysis in Medicine, CADAM 1995. IJS Scientific Publishing, Ljubljana (1995)
18. Delen*, D., Walker, G., Kadam, A.: Predicting breast cancer survivability: a comparison of three data mining methods. Artificial Intelligence in Medicine, doi:10.1016/j.artmed.2004.07.002
19. Hedberg, S.R.: The data gold rush. Byte, 83-88 (October 1995)
20. Magoulas, G.D., Prentza, A.: Machine learning in medical applications
21. Lee, S.J., Siau, K.: A review of data mining techniques. Industrial Management and Data Systems 101(1), 41-46 (2001)
22. Pyle, D.: Data Preparation for Data Mining. Morgan Kaufmann Publishers, San Francisco (1999)
23. Boser, B., Guyon, I., Vapnik, V.: A training algorithm for optimal margin classifiers. In: Proceedings of the Fifth Annual Workshop on Computational Learning Theory, pp. 144-152 (1992)
24. Weston, J., Mukherjee, S., Chapelle, O., Pontil, M., Poggio, T., Vapnik, V.N.: Feature selection for SVMs. In: Leen, T., Dietterich, T., Tresp, V. (eds.) Advances in Neural Information Processing Systems 13, pp. 668-674 (2001)
25. Molina, L.C., Belanche, L., Nebot, A.: Attribute Selection Algorithms: A survey and experimental evaluation. In: Proceedings of 2nd IEEE's KDD 2002, pp. 306-313 (2002)
26. Guyon, I., Elisseeff, A.: An Introduction to Variable and Feature Selection. Journal of Machine Learning Research 3, 1157-1182 (2003)
27. Koller, D., Sahami, M.: Towards optimal feature selection. In: 13th International Conference on Machine Learning, Bari, Italy, pp. 284-292 (1996)
28. Richards, G., Rayward-Smith, V.J., Sonksen, P.H., Carey, S., Weng, C.: Data mining for indicators of early mortality in a database of clinical records. Artif. Intell. Med. 22, 215-231 (2001)
29. Siedlecki, W., Sklansky, J.: On automatic feature selection. International Journal of Pattern Recognition and Artificial Intelligence 2(2), 197-220 (1988)
30. Kohavi, R., John, G.: Wrapper for Feature Subset Selection. Artificial Intelligence 97(1-2), 273-324 (1997)
31. Almuallim, H., Dietterich, T.G.: Efficient algorithms for identifying relevant features. In: Proceedings of the Ninth Canadian Conference on Artificial Intelligence. Morgan Kaufmann, Vancouver (1992)
32. Kohavi, R., John, G.: Wrapper for Feature Subset Selection. Artificial Intelligence 97(1-2), 273-324 (1997)
33. Langley, P.: Selection of Relevant Features in Machine Learning. In: Proc. AAAI Fall Symp. Relevance (1994)
34. Liu, H., Yu, L.: Feature Selection for Data Mining
35. Vapnik, V.: Statistical Learning Theory. Wiley, New York (1998)
36. Evgeniou, T., Pontil, M., Papageorgiou, C., Poggio, T.: Image representations for object detection using kernel classifiers. In: Asian Conference on Computer Vision (2000)
37. Mukherjee, S., Tamayo, P., Slonim, D., Verri, A., Golub, T., Mesirov, J., Poggio, T.: Support vector machine classification of microarray data. AI Memo 1677, Massachusetts Institute of Technology (1999)
38. Furey, T.S., Cristianini, N., Duffy, N., Bednarski, D.W., Schummer, M., Haussler, D.: Support vector machine classification and validation of cancer tissue samples using Microarray expression data. Bioinformatics 16, 906-914 (2000)
39. Takeuchi, K., Collier, N.: Bio-medical entity extraction using support vector machines. Artif. Intell. Med. 33, 125-137 (2005)
40. Cohen, G., Hilario, M., Sax, H., Hugonnet, S., Geissbuhler, A.: Learning from imbalanced data in surveillance of nosocomial infection. Artif. Intell. Med. 37, 7-18 (2006)
41. Ali, S., Smith, K.A.: Automatic parameter selection for polynomial kernel. In: Proc. of the IEEE Int. Conf. on Information Reuse and Integration (IRI 2003), Las Vegas, NV, USA, October 27-29, pp. 243-249 (2003)
42. Imbault, F., Lebart, K.: A stochastic optimization approach for parameter tuning of support vector machines. In: Proc. of the 17th Int. Conf. on Pattern Recognition (ICPR 2004), Cambridge, UK, vol.4, pp. 597-600 (2004)
43. Schittkowski, K.: Optimal parameter selection in support vector machines. Journal of Industrial and Management Optimization 1(4), 465-476 (2005)
44. John, G.H., Kohavi, R., Pfleger, K.: Irrelevant feature and the subset selection problem. In: asnd Hirsh H. Cohen, W.W. (ed.) Machine Learning: Proceedings of the Eleventh International Conference, New Brunswick, N.J., pp. 121-129. Rutgers University (1994)
45. Herron: Machine Learning for Medical Decision Support: Evaluating Diagnostic Performance of Machine Learning classification Algorithms
46. Huang, J., Ling, C.X.: Using AUC and Accuracy in Evaluating Learning Algorithms. IEEE Transactions on Knowledge and Data Engineering 17(3) (2005)
47. Huang, C.-L., Liao, H.-C., Chen, M.-C.: Prediction model building and feature selection with support vector machines in breast cancer diagnosis. Expert Systems with Applications, 578-587 (2008), doi:10.1016/j.eswa.2006.09.041 (Pubitemid 46756285)
48. EL-Manzalawy, Y., Honavar, V.: WLSVM: Integrating LibSVM into Weka Environment (2005), http://www.cs.iastate.edu/yasser/wlsvm