Boosted SVM for extracting rules from imbalanced data in application to prediction of the post-operative life expectancy in the lung cancer patients

Applied Soft Computing Journal

Volumn 14, Issue PART A, 2014, Pages 99-108

  Ziȩba, Maciej ^a   Tomczak, Jakub M ^a   Lubicz, Marek ^a   Świa̧tek, Jerzy ^a  

^a WROCLAW UNIVERSITY OF TECHNOLOGY   (Poland)

Author keywords

Boosted SVM; Decision rules; Imbalanced data; Post operative life expectancy prediction

Indexed keywords

BOOSTED SVM; DECISION RULES; ENSEMBLE CLASSIFIERS; EXTRACTING RULES; IMBALANCED DATA; IMBALANCED DATA PROBLEMS; LIFE EXPECTANCIES; SUPPORT VECTORS MACHINE;

BIOLOGICAL ORGANS; DISEASES; MEDICAL APPLICATIONS;

FORECASTING;

EID: 84888298686     PISSN: 15684946     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.asoc.2013.07.016     Document Type: Article

References (58)

1. P.J. Garcia-Laencina, J.L. Sancho-Gomez, and A.R. Figueiras-Vidal Pattern classification with missing data: a review Neural Computing and Applications 19 2009 263 282
2. T. Dietterich Machine learning for sequential data: a review T. Caelli, A. Amin, R.P.W. Duin, M. Kamel, D. de Ridder, Structural, Syntactic, and Statistical Pattern Recognition 2002 Springer Berlin Heidelberg 227 246
3. H. He, and E.A. Garcia Learning from imbalanced data IEEE Transactions on Knowledge and Data Engineering 21 2009 1263 1284
4. M. Galar, A. Fernández, E. Barrenechea, H. Bustince, and F. Herrera A review on ensembles for the class imbalance problem: bagging-, boosting-, and hybrid-based approaches IEEE Transactions on Systems, Man and Cybernetics - Part C: Applications and Reviews 42 2012 3358 3378
5. N.V. Chawla, K.W. Bowyer, and L.O. Hall SMOTE: Synthetic Minority Over-samplin (Pubitemid 43057176)
6. H. Han, W.-Y. Wang, and B.-H. Mao Borderline-SMOTE: a new over-sampling method in imbalanced data sets learning D.-S. Huang, X.-P. Zhang, G.-B. Huang, Advances in Intelligent Computing 2005 878 887 (Pubitemid 41491129)
7. I. Zhang, and J. Mani KNN approach to unbalanced data distributions: a case study involving information extraction Proceedings of International Conference on Machine Learning (ICML 2003), Workshop Learning from Imbalanced Data Sets 2003
8. S. García, A. Fernández, and F. Herrera Enhancing the effectiveness and interpretability of decision tree and rule induction classifiers with evolutionary training set selection over imbalanced problems Applied Soft Computing 9 2009 1304 1314
9. N. Chawla, A. Lazarevic, L. Hall, and K. Bowyer Smoteboost: improving prediction of the minority class in boosting N. Lavrac, D. Gamberger, H. Blockeel, L. Todorovski, Knowledge Discovery in Databases: PKDD 2003 2003 Springer Berlin Heidelberg 107 119 (Pubitemid 37231089)
10. S. Wang, and X. Yao Diversity analysis on imbalanced data sets by using ensemble models 2009 IEEE Symposium on Computational Intelligence and Data Mining Proceedings 2009 324 331
11. E. Chang, B. Li, G. Wu, and K. Goh Statistical learning for effective visual information retrieval IEEE Proceedings of the 2003 International Conference on Image Processing, vol. 3 2003 609 613
12. D. Tao, X. Tang, X. Li, and X. Wu Asymmetric bagging and random subspace for support vector machines-based relevance feedback in image retrieval IEEE Transactions on Pattern Analysis and Machine Intelligence 28 2006 1088 1099
13. Y. Tang, B. Jin, and Y. Zhang Granular support vector machines with association rules mining for protein homology prediction Artificial Intelligence in Medicine 35 2005 121 134 (Pubitemid 41242716)
14. Y. Tang, B. Jin, Y. Zhang, H. Fang, and B. Wang Granular support vector machines using linear decision hyperplanes for fast medical binary classification IEEE Proceedings of the 14th IEEE International Conference on Fuzzy Systems 2005 138 142 (Pubitemid 41189496)
15. Y. Tang, and Y. Zhang Granular svm with repetitive undersampling for highly imbalanced protein homology prediction Y.-Q. Zhang, T.Y. Lin, 2006 IEEE International Conference on Granular Computing 2006 457 460 (Pubitemid 44761948)
16. Y. Tang, Y. Zhang, and Z. Huang Development of two-stage svm-rfe gene selection strategy for microarray expression data analysis IEEE/ACM Transactions on Computational Biology and Bioinformatics 4 2007 365 381 (Pubitemid 47274713)
17. S. Ertekin, J. Huang, L. Bottou, and L. Giles Learning on the border: active learning in imbalanced data classification Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval 2007 ACM New York 127 136
18. S. Ertekin, J. Huang, and C. Giles Active learning for class imbalance problem Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management 2007 ACM 823 824 (Pubitemid 350165088)
19. W. Fan, S. Stolfo, J. Zhang, and P Chan Adacost: misclassification cost-sensitive boosting I. Bratko, S. Dzeroski, Proceedings of the Sixteenth International Conference on Machine Learning (ICML 1999) 1999 Morgan Kaufmann 97 105
20. K. Ting A comparative study of cost-sensitive boosting algorithms P. Langley, Proceedings of the Seventeenth International Conference on Machine Learning (ICML 2000) 2000 Morgan Kaufmann 983 990
21. M. Joshi, V. Kumar, and R. Agarwal Evaluating boosting algorithms to classify rare classes: Comparison and improvements N. Cercone, T.Y. Lin, X. Wu, Proceedings. 2001 IEEE International Conference on Data Mining 2001 IEEE Los Alamitos 257 264
22. Y. Sun, M. Kamel, A. Wong, and Y. Wang Cost-sensitive boosting for classification of imbalanced data Pattern Recognition 40 2007 3358 3378 (Pubitemid 47223287)
23. K. Morik, P. Brockhausen, and T. Joachims Combining statistical learning with a knowledge-based approach-a case study in intensive care monitoring I. Bratko, S. Dzeroski, Proceedings of the Sixteenth International Conference on Machine Learning (ICML 1999) 1999 Morgan Kaufmann 268 277
24. K. Veropoulos, C. Campbell, and N. Cristianini Controlling the sensitivity of support vector machines Proceedings of the 16th International Joint Conference on Artificial Intelligence, (IJCAI999), Workshop ML3, vol. 1999 1999 55 60
25. B. Wang, and N. Japkowicz Boosting support vector machines for imbalanced data sets Knowledge and Information Systems 25 2010 1 20
26. A. Tickle, R. Andrews, M. Golea, and J. Diederich The truth will come to light: directions and challenges in extracting the knowledge embedded within trained artificial neural networks IEEE Transactions on Neural Networks 9 1998 1057 1068 (Pubitemid 128742391)
27. J. Chorowski, and J.M. Zurada Extracting rules from neural networks as decision diagrams IEEE Transactions on Neural Networks 22 2011 2435 2446
28. H. Nú nez, C. Angulo, and A. Català Rule extraction from support vector machines Proceedings of the European Symposium on Artificial Neural Networks 2002 107 112
29. M. Craven, and J. Shavlik Rule extraction: where do we go from here?, Technical Report, University of Wisconsin Machine Learning Research Group Working Paper 1999
30. N. Barakat, and J. Diederich Eclectic rule-extraction from support vector machines International Journal of Computational Intelligence 2 2005 59 62
31. I. Steinwart, and A. Christmann Support Vector Machines 2008 Springer New York
32. H. Masnadi-Shirazi, and N. Vasconcelos Risk minimization, probability elicitation, and cost-sensitive SVMs J. Fürnkranz, T. Joachims, Proceedings of the 27th International Conference on Machine Learning (ICML-10) 2010 Omnipress 204 213
33. V. Vapnik Statistical Learning Theory 1998 John Wiley and Sons, Inc. New York
34. E. Osuna, R. Freund, and F. Girosi An improved training algorithm for support vector machines J. Principe, L. Gile, N. Morgan, E. Wilson, Neural Networks for Signal Processing VII, Proceedings of the 1997 IEEE Signal Processing Workshop 1997 IEEE 276 285
35. J. Platt Fast training of support vector machines using sequential minimal optimization B. Schölkopf, C.J.C. Burges, A.J. Smola, Advances in Kernel Methods: Support Vector Learning 1999 MIT Press Cambridge
36. S. Keerthi, S. Shevade, C. Bhattacharyya, and K. Murthy Improvements to platt's smo algorithm for svm classifier design Neural Computation 13 2001 637 649 (Pubitemid 33595014)
37. Y. Freund, R.E. Schapire, and M. Hill Experiments with a New Boosting Algorithm L. Saitta, Machine Learning, Proceedings of the Thirteenth International Conference (ICML'96) 1996 Morgan Kaufmann
38. W.W. Cohen Fast effective rule induction A. Prieditis, S. Russell, in Proceedings of the 12th International Conference on Machine Learning 1995 Morgan Kaufmann Tahoe City, CA 115 123
39. J. Alcalá, A. Fernández, J. Luengo, J. Derrac, S. García, L. Sánchez, and F. Herrera Keel data-mining software tool: Data set repository, integration of algorithms and experimental analysis framework Journal of Multiple-valued Logic and Soft Computing 17 2010 255 287
40. A. Fernández, J. Luengo, J. Derrac, J. Alcalá-Fdez, and F. Herrera Implementation and integration of algorithms into the keel data-mining software tool E. Corchado, H. Yin, Intelligent Data Engineering and Automated Learning-IDEAL 2009 2009 Springer Berlin 562 569
41. Y. Tang, Y. Zhang, N. Chawla, and S. Krasser Svms modeling for highly imbalanced classification IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 39 2009 281 288
42. C. Seiffert, T. Khoshgoftaar, J. Van Hulse, and A. Napolitano Rusboost A hybrid approach to alleviating class imbalance, IEEE Transactions on Systems Man and Cybernetics, Part A: Systems and Humans 40 2010 185 197
43. M. Shapiro, S.J. Swanson, C.D. Wright, C. Chin, S. Sheng, J. Wisnivesky, and T.S. Weiser Predictors of major morbidity and mortality after pneumonectomy utilizing the society for thoracic surgeons general thoracic surgery database Annals of Thoracic Surgery 90 2010 927 935
44. U. Aydogmus, L. Cansever, Y. Sonmezoglu, K. Karapinar, C.I. Kocaturk, and M.A. Bedirhan The impact of the type of resection on survival in patients with n1 non-small-cell lung cancers European Journal of Cardio-Thoracic Surgery 37 2010 446 450
45. P. Icard, M. Heyndrickx, L. Guetti, F. Galateau-Salle, P. Rosat, J.P. Le Rochais, and J.-L. Hanouz Morbidity, mortality and survival after 110 consecutive bilobectomies over 12 years Interactive Cardiovascular and Thoracic Surgery 16 2013 179 185
46. D. Shahian, and F. Edwards Statistical risk modeling and outcomes analysis Annals of Thoracic Surgery 86 2008 1717 1720
47. R. Berrisford, A. Brunelli, G. Rocco, T. Treasure, and M. Utley The european thoracic surgery database project: modelling the risk of in-hospital death following lung resection European Journal of Cardio-Thoracic Surgery 28 2005 306 311 (Pubitemid 41020703)
48. P.E. Falcoz, M. Conti, L. Brouchet, S. Chocron, M. Puyraveau, M. Mercier, J.P. Etievent, and M. Dahan The thoracic surgery scoring system (thoracoscore): risk model for in-hospital death in 15,183 patients requiring thoracic surgery The Journal of Thoracic and Cardiovascular Surgery 133 2007 325 332 (Pubitemid 46136251)
49. A. Barua, S.D. Handagala, L. Socci, B. Barua, M. Malik, N. Johnstone, and A.E. Martin-Ucar Accuracy of two scoring systems for risk stratification in thoracic surgery Interactive Cardiovascular and Thoracic Surgery 14 2012 556 559
50. G. Rocco ecomment. re: Accuracy of two scoring systems for risk stratification in thoracic surgery Interactive Cardiovascular and Thoracic Surgery 14 2012 559
51. E. Rivo, J. de la Fuente, Á. Rivo, E. García-Fontán, M.-Á. Ca nizares, and P. Gil Cross-industry standard process for data mining is applicable to the lung cancer surgery domain, improving decision making as well as knowledge and quality management Clinical and Translational Oncology 14 2012 73 79
52. N. Voznuka, H. Granfeldt, A. Babic, M. Storm, U. Lönn, and H.C. Ahn Report generation and data mining in the domain of thoracic surgery Journal of Medical Systems 28 2004 497 509 (Pubitemid 39663117)
53. J. Dowie, and M. Wildman Choosing the surgical mortality threshold for high risk patients with stage la non-small cell lung cancer: Insights from decision analysis Thorax 57 2002 7 10 (Pubitemid 34081536)
54. M.K. Ferguson, J. Siddique, and T. Karrison Modeling major lung resection outcomes using classification trees and multiple imputation techniques European Journal of Cardio-Thoracic Surgery 34 2008 1085 1089
55. H. Esteva, T.G. Nú nez, and R.O. Rodríguez Neural networks and artificial intelligence in thoracic surgery Thoracic Surgery Clinics 17 2007 359 367 (Pubitemid 47628754)
56. G. Santos-GarcIa, G. Varela, N. Novoa, and M.F. Jiménez Prediction of postoperative morbidity after lung resection using an artificial neural network ensemble Artificial Intelligence in Medicine 30 2004 61 69 (Pubitemid 37543648)
57. C.-Y. Lee, and Z.-J. Lee A novel algorithm applied to classify unbalanced data Applied Soft Computing 12 2012 2481 2485
58. Y. Yang, and J.O. Pedersen A comparative study on feature selection in text categorization D.H. Fisher, Proceedings of the Fourteenth International Conference on Machine Learning (ICML 1997) 1997 Morgan Kaufmann 412 420