Support vector machine for outlier detection in breast cancer survivability prediction

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 4977 LNCS, Issue , 2008, Pages 99-109

  Thongkam, Jaree ^a   Xu, Guandong ^a   Zhang, Yanchun ^a   Huang, Fuchun ^a  

^a VICTORIA UNIVERSITY   (Australia)

Author keywords

Breast cancer survivability; C Support Vector Classification Filter (C SVCF); Outlier detection system

Indexed keywords

ADAPTIVE BOOSTING; ANOMALY DETECTION; DISEASES; FILTRATION; FORECASTING; PREDICTIVE ANALYTICS; STATISTICS; SUPPORT VECTOR MACHINES;

BREAST CANCER; DATA MINING ALGORITHM; ENSEMBLE FILTER; OUTLIER DETECTION SYSTEMS; PREDICTION MODEL; RECEIVER OPERATING CHARACTERISTIC CURVES; ROBUST PREDICTIONS; SUPPORT VECTOR CLASSIFICATION;

DATA MINING;

EID: 85099427126     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-89376-9_10     Document Type: Conference Paper

References (28)

1. Tsumoto, S.: Problems with Mining Medical Data. In: The Twenty-Fourth Annual International Conference on Computer Software and Applications, pp. 467-468 (2000)
2. Li, J., Fu, A.W.-C., He, H., Chen, J., Kelman, C: Mining Risk Patterns in Medical Data. In: Proc. the Eleventh ACM SIGKDD International Conference on Knowledge Discovery in Data Mining, pp. 770-775 (2005)
3. Verbaeten, S., Assche, A.V.: Ensemble Methods for Noise Elimination in Classification Problems. In: Windeatt, T., Roli, F. (eds.) MCS 2003. LNCS, vol. 2709, pp. 317-325. Springer, Heidelberg (2003)
4. Brodley, C.E., Friedl, M.A.: Identifying and Eliminating Mislabeled Training Instances. J. Artificial Intelligence Research 1 (1996)
5. Brodley, C.E., Friedl, M.A.: Identifying Mislabeled Training Data. J. Artificial Intelligence Research. 11, 131-167 (1999)
6. John, G.H.: Robust Decision Trees: Removing Outliers from Databases. In: Proc. the First International Conference on Knowledge Discovery and Data Mining, pp. 174-179. AAAI Press, Menlo Park (1995)
7. Teng, CM.: Applying Noise Handling Techniques to Genomic Data: A Case Study. In: Proc. the Third IEEE International Conference on Data Mining, p. 743 (2003)
8. Muhlenbach, F., Lallich. S., Zighed, D.A.: Identifying and Handling Mislabelled Instances. J. Intelligent Information Systems. 22(1), 89-109 (2004)
9. Hristovski, D., Peterlin, B., Mitchell, J.A., Humphrey, S.M.: Using Literature-Based Discovery to Identify Disease Candidate Genes. J. Medical Informatics. 74, 289-298 (2005)
10. Blanco, Á., Ricket, A.M., Martín-Merino, M.: Combining SVM Classifiers for Email Anti-Spam Filtering. In: Proc. the Ninth International Work-Conference on Artificial Neural Networks, pp. 903-910. Springer, Heidelberg (2007)
11. Han, J., Kamber, M.: Data Mining: Concepts and Techniques. Morgan Kaufmann, Elsevier Science, San Francisco (2006)
12. Vapnik, V.: Statistical Learning Theory. Wiley, New York (1998)
13. Yin, Z., Yin, P., Sun, F., Wu, H.: A Writer Recognition Approach Based on SVM. In: Multi Conference on Computational Engineering in Systems Applications, pp. 581-586 (2006)
14. Lallich, S., Muhlenbach, F., Zighed, D.A.: Improving Classification by Removing or Re-labeling Mislabeled Instances. In: Proc. the Thirteen International Symposium on Foundations of Intelligent Systems, pp. 5-15 (2002)
15. Sun, J.-w., Zhao, F.-y., Wang, C.-j., Chen, S.-f.: Identifying and Correcting Mislabeled Training Instances. In: Future Generation Communication and Networking, pp. 244-250 (2007)
16. Xiao, Y., Khoshgoftaar, T.M., Seliya, N.: The Partitioning- and Rule-Based Filter for Noise Detection. In: Proc. IEEE International Conference on Information Reuse and Integration, pp. 205-210 (2005)
17. Yi, W., Fuyong, W.: Breast Cancer Diagnosis Via Support Vector Machines. In: Proc. the Twenty Fifth Chinese Control Conference, pp. 1853-1856 (2006)
18. Kohavi, R.: A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection. In: Proc. the International Joint Conference on Artificial Intelligence, pp. 1137-1143 (1995)
19. Thongkam, J., Xu, G., Zhang, Y.: An Analysis of Data Selection Methods on Classifiers Accuracy Measures. J. Korn Ken University (2007)
20. Huang, J., Ling, C.X.: Using AUC and Accuracy in Evaluating Learning Algorithms. IEEE Transactions on Knowledge and Data Engineering, 299-310 (2005)
21. Hand, D.J., Till, J.R.: A Simple Generalisation of the Area under the ROC Curve for Multiple Class Classification Problems J. Machine Learning 45, 171-186 (2001)
22. He, X., Frey, E.C.: Three-Class ROC Analysis-the Equal Error Utility Assumption and the Optimality of Three-Class ROC Surface Using the Ideal Observer. IEEE Transactions on Medical Imaging, 979-986 (2006)
23. Woods, K., Bowyer, K.W.: Generating ROC Curves for Artificial Neural Networks. IEEE Transactions on Medical Imaging, 329-337 (1997)
24. Jiang, Y.: Uncertainty in the Output of Artificial Neural Networks. In: International Joint Conference on Neural Networks, pp. 2551-2556 (2007)
25. Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann, San Francisco (2005)
26. Chang, C.-C., Lin, C.-J.: Libsvm-a Library for Support Vector Machines, http://www.csie.ntu.edu.tw/~cjlin/libsvm
27. Gamberger, D., Šmuc, T., Marić, I.: Noise Detection and Elimination in Data Preprocessing Experiments in Medical Domains. J. Applied Artificial Intelligence. 14, 205-223 (2000)
28. Khoshgoftaar, T.M., Seliya, N., Gao, K.: Rule-Based Noise Detection for Software Measurement Data. In: Proc. IEEE International Conference on Information Reuse and Integration, pp. 302-307 (2004)