Interpretation of mammograms with rotation forest and PCA

SACI 2011 - 6th IEEE International Symposium on Applied Computational Intelligence and Informatics, Proceedings

Volumn , Issue , 2011, Pages 571-575

  Novakovic, J ^a   Veljovic, A ^b  

^a Megatrend University   (Serbia)

^b UNIVERSITY OF KRAGUJEVAC   (Serbia)

Author keywords

classification accuracy; decision tree; mammogram; PCA; rotation forest

Indexed keywords

BASE CLASSIFIERS; BREAST BIOPSIES; CLASSIFICATION ACCURACY; CLASSIFICATION SYSTEM; MAMMOGRAM; MAMMOGRAPHIC MASS; PCA; TREES ALGORITHM; UNSUPERVISED LEARNING METHOD;

ARTIFICIAL INTELLIGENCE; BIOPSY; DECISION TREES; INFORMATION SCIENCE; MAMMOGRAPHY; PLANT EXTRACTS; ROTATION; UNSUPERVISED LEARNING; X RAY SCREENS;

PRINCIPAL COMPONENT ANALYSIS;

EID: 79959930784     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/SACI.2011.5873068     Document Type: Conference Paper

References (23)

1. J. Nahar, Y.P. Chen, and S. Ali, "Kernel-based naive bayes classifier for breast cancer prediction", Journal of Biological System, 15(1):17-25, 2007. (Pubitemid 46340530)
2. A. Bellaachia, and E. Guven, "Predicting breast cancer survivability using data mining techniques", Scientific Data Mining Workshop, in Conjunction with the 2006 SIAM Conference on Data Mining, 2006.
3. D. Delen, G. Walker, and A. Kadam, "Predicting breast cancer survivability: a comparison of three data mining methods", Artificial Intelligence in Medicine, 34(2):113-127, 2005. (Pubitemid 40719029)
4. D. Soria, J. M. Garibaldi, E. Biganzoli, and I. O. Ellis, "A comparison of three different methods for classification of breast cancer data", Seventh International Conference on Machine Learning and Applications, IEEE Computer Society, 2008.
5. R. Elter, Schulz-Wendtland, and T. Wittenberg, "The prediction of breast cancer biopsy outcomes using two CAD approaches that both emphasize an intelligible decision process", Medical Physics 34(11), pp. 4164-4172, 2007. (Pubitemid 350029638)
6. J. J., Rodriguez, L. I. Kuncheva, and C. J. Alonso, "Rotation Forest: A New Classifier Ensemble Method", IEEE Transactions on pattern analysis and machine intelligence, vol. 28, no. 10, October 2006.
7. Y. Freund, and L. Mason, "The alternating decision tree learning algorithm", Proceeding of the Sixteenth International Conference on Machine Learning, Bled, Slovenia, 124-133, 1999.
8. J. Friedman, T. Hastie, and R. Tibshirani, "Additive logistic regression: A statistical view of boosting", Annals of statistics, 28(2):337-407, 2000. (Pubitemid 33227445)
9. G. Holmes, B. Pfahringer, R. Kirkby, E. Frank, and M. Hall, "Multiclass alternating decision trees", ECML, 161-172, 2001.
10. N. Landwehr, M. Hall, and E. Frank, "Logistic Model Trees", Machine Learning, 95(1-2):161-205, 2005. (Pubitemid 40890416)
11. M. Sumner, E. Frank, and M. Hall, "Speeding up Logistic Model Tree Induction", 9th European Conference on Principles and Practice of Knowledge Discovery in Databases, 675-683, 2005.
12. L. Breiman, "Random Forests", Machine Learning. 45(1):5-32, 2001. (Pubitemid 32933532)
13. J. Han, and M. Kamber, Data Mining: Concepts and Techniques, Morgan Kaufmann, 2001.
14. F.H. Foley, and J.W. Sammon, "An Optimal Set of Discriminant Vectors," IEEE Trans. Computers, vol. 24, no. 3, pp. 281-289, Mar.1975.
15. K. Fukunaga, and W.L.G. Koontz, "Application of the Karhunen-Loeve Expansion to Feature Selection and Ordering," IEEE Trans. Computers, vol. 19, no. 4, pp. 311-318, Apr. 1970.
16. J. Kittler, and P.C. Young, "A New Approach to Feature Selection Based on the Karhunen-Loeve Expansion," Pattern Recognition, vol. 5, no. 4, pp. 335-352, Dec. 1973.
17. F. van der Heijden, R.P.W. Duin, D. de Ridder, and D.M.J, Tax, Classification, Parameter Estimation and State Estimation, Wiley, 2004.
18. A. Webb, Statistical Pattern Recognition, London: Arnold, 1999.
19. X.Z. Fern, and C.E. Brodley, "Random Projection for High Dimensional Data Clustering: A Cluster Ensemble Approach," Proc. 20th Int'l Conf. Machine Learning (ICML), pp. 186-193, 2003.
20. M. Skurichina, and R.P.W. Duin, "Combining Feature Subsets in Feature Selection," Proc. Sixth Int'l Workshop Multiple Classifier Systems (MCS '05), pp. 165-175, 2005. (Pubitemid 41422615)
21. I.T. Jolliffe, Principal Component Analysis, Springer-Verlag, New York, 1986.
22. M. Kantardzic, Data Mining: Concepts, Models, Methods, and Algorithms, John Wiley & Sons, 2003.
23. M. Elter, R. Schulz-Wendtland, and T. Wittenberg, "The prediction of breast cancer biopsy outcomes using two CAD approaches that both emphasize an intelligible decision process", Medical Physics 34(11), pp. 4164-4172, 2007. (Pubitemid 350029638)