-
1
-
-
79958056817
-
-
World Health Organization Retrieved September 22, 2010
-
World Health Organization, Quick cancer facts. Retrieved September 22, 2010 http://www.who.int/cancer/en/
-
Quick Cancer Facts
-
-
-
2
-
-
81355164576
-
Breast cancer statistics, 2011
-
Breast Cancer Statistics, 2011. DeSantis C, Siegel R, Bandi P, Jemal A, Cancer J Clin 2011 61 409 418
-
(2011)
Cancer J Clin
, vol.61
, pp. 409-418
-
-
Desantis, C.1
Siegel, R.2
Bandi, P.3
Jemal, A.4
-
4
-
-
0032730109
-
Artificial neural networks applied to survival prediction in breast cancer
-
10.1159/000012061 10575312
-
Artificial neural networks applied to survival prediction in breast cancer. Lundin M, Lundin J, Burke HB, Toikkanen S, Pylkkänen L, Joensuu H, Oncology 1999 57 281 286 10.1159/000012061 10575312
-
(1999)
Oncology
, vol.57
, pp. 281-286
-
-
Lundin, M.1
Lundin, J.2
Burke, H.B.3
Toikkanen, S.4
Pylkkänen, L.5
Joensuu, H.6
-
7
-
-
74949110619
-
Comparison of three data mining techniques with genetic algorithm in the analysis of breast cancer data
-
Comparison of three data mining techniques with genetic algorithm in the analysis of breast cancer data. Chang WP, Liou DM, J Telemed Telecare 2008 9 1 26
-
(2008)
J Telemed Telecare
, vol.9
, pp. 1-26
-
-
Chang, W.P.1
Liou, D.M.2
-
8
-
-
19344364327
-
Predicting breast cancer survivability: A comparison of three data mining methods
-
DOI 10.1016/j.artmed.2004.07.002, PII S0933365704001010
-
Predicting breast cancer survivability: a comparison of three data mining methods. Delen D, Walker G, Kadam A, Artif Intell Med 2005 34 113 127 10.1016/j.artmed.2004.07.002 15894176 (Pubitemid 40719029)
-
(2005)
Artificial Intelligence in Medicine
, vol.34
, Issue.2
, pp. 113-127
-
-
Delen, D.1
Walker, G.2
Kadam, A.3
-
9
-
-
84888327297
-
Predicting breast cancer survivability using data mining techniques
-
Predicting breast cancer survivability using data mining techniques. Bellaachia A, Guven E, Age 2006 58 10 110
-
(2006)
Age
, vol.58
, pp. 10-110
-
-
Bellaachia, A.1
Guven, E.2
-
12
-
-
9444297357
-
Smoteboost: Improving prediction of the minority class in boosting
-
Knowledge Discovery in Databases: PKDD 2003
-
SMOTEBoost: Improving prediction of the minority class in boosting. Chawla NV, Lazarevic A, Hall LO, Bowyer KW, Proceedings of the 7th European conference on principles and practice of knowledge discovery in database Berlin: Springer 2003 107 119 (Pubitemid 37231089)
-
(2003)
LEcture Notes In Computer Science
, Issue.2838
, pp. 107-119
-
-
Chawla, N.V.1
Lazarevic, A.2
Hall, L.O.3
Bowyer, K.W.4
-
13
-
-
68549133155
-
Learning from imbalanced data
-
Learning from imbalanced data. He H, Garcia E, IEEE Trans Knowl Data Eng 2009 21 9 1263 1284
-
(2009)
IEEE Trans Knowl Data Eng
, vol.21
, Issue.9
, pp. 1263-1284
-
-
He, H.1
Garcia, E.2
-
14
-
-
78650716557
-
Classification of imbalanced data sets by using the hybrid re-sampling algorithm based on isomap
-
10.1007/978-3-642-04843-2-31
-
Classification of imbalanced data sets by using the hybrid re-sampling algorithm based on isomap. Gu Q, Cai Z, Ziu L, In LNCS, Adv Comput Intelligence 2009 5821 287 296 10.1007/978-3-642-04843-2-31
-
(2009)
LNCS, Adv Comput Intelligence
, vol.5821
, pp. 287-296
-
-
Gu, Q.1
Cai, Z.2
Ziu, L.3
-
16
-
-
79960872876
-
Predicting disease risks from highly imbalanced data using random forest
-
10.1186/1472-6947-11-51 21801360
-
Predicting disease risks from highly imbalanced data using random forest. Khalilia M, Chakraborty S, Popescu M, BMC Med Inform Decis Mak 2011 11 51 10.1186/1472-6947-11-51 21801360
-
(2011)
BMC Med Inform Decis Mak
, vol.11
, pp. 51
-
-
Khalilia, M.1
Chakraborty, S.2
Popescu, M.3
-
17
-
-
84874398938
-
Improving sensitivity of machine learning methods for automated case identification from free-text electronic medical records
-
10.1186/1472-6947-13-30 23452306
-
Improving sensitivity of machine learning methods for automated case identification from free-text electronic medical records. Afzal Z, Schuemie MJ, van Blijderveen JC, Sen EF, Sturkenboom MCJM, Kors JA, BMC Med Inform Decis Mak 2013 13 30 10.1186/1472-6947-13-30 23452306
-
(2013)
BMC Med Inform Decis Mak
, vol.13
, pp. 30
-
-
Afzal, Z.1
Schuemie, M.J.2
Van Blijderveen, J.C.3
Sen, E.F.4
Sturkenboom, M.5
Kors, J.A.6
-
20
-
-
84971339978
-
An insight into classification with imbalanced data: Empirical results and current trends on using data intrinsic characteristics
-
in press
-
An insight into classification with imbalanced data: Empirical results and current trends on using data intrinsic characteristics. Lopez V, Fernández A, García S, Palade V, Herrera F, Inform Sci in press
-
Inform Sci
-
-
Lopez, V.1
Fernández, A.2
García, S.3
Palade, V.4
Herrera, F.5
-
21
-
-
34547993162
-
C4.5, class imbalance, and cost sensitivity: Why under-sampling beats over-sampling
-
C4.5, class imbalance, and cost sensitivity: Why under-sampling beats over-sampling. Drummond C, Holte RC, Proceeding of Workshop on Learning from Imbalanced Datasets II, ICML 2003 1 8
-
(2003)
Proceeding of Workshop on Learning from Imbalanced Datasets II, ICML
, pp. 1-8
-
-
Drummond, C.1
Holte, R.C.2
-
22
-
-
0346586663
-
SMOTE: Synthetic minority over-sampling technique
-
SMOTE: Synthetic minority over-sampling technique. Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP, J Artif Intell Res 2002 16 321 357 (Pubitemid 43057176)
-
(2002)
Journal of Artificial Intelligence Research
, vol.16
, pp. 321-357
-
-
Chawla, N.V.1
Bowyer, K.W.2
Hall, L.O.3
Kegelmeyer, W.P.4
-
23
-
-
39749147033
-
Protein classification with imbalanced data
-
DOI 10.1002/prot.21870
-
Protein classification with imbalanced data. Zhao XM, Li X, Chen L, Aihara K, Proteins 2007 70 4 1125 1132 10.1002/prot.21870 (Pubitemid 351304071)
-
(2008)
Proteins: Structure, Function and Genetics
, vol.70
, Issue.4
, pp. 1125-1132
-
-
Zhao, X.-M.1
Li, X.2
Chen, L.3
Aihara, K.4
-
24
-
-
35148838577
-
Applying novel resampling strategies to software defect prediction
-
DOI 10.1109/NAFIPS.2007.383813, 4271036, NAFIPS 2007: 2007 Annual Meeting of the North American Fuzzy Information Processing Society
-
Applying novel resampling strategies to software defect prediction. Pelayo L, Dick S, Proceedings of the annual meeting of the North American fuzzy information processing society San Diego: IEEE 2007 69 72 (Pubitemid 47533936)
-
(2007)
Annual Conference of the North American Fuzzy Information Processing Society - NAFIPS
, pp. 69-72
-
-
Pelayo, L.1
Dick, S.2
-
25
-
-
47949103719
-
The effects of over and under sampling on fault-prone module detection
-
Madrid: IEEE
-
The effects of over and under sampling on fault-prone module detection. Kamei Y, Monden A, Matsumoto S, Kakimoto T, Matsumoto K, Proceedings of First International Symposium on Empirical Software Engineering and Measurement Madrid: IEEE 2007 196 204
-
(2007)
Proceedings of First International Symposium on Empirical Software Engineering and Measurement
, pp. 196-204
-
-
Kamei, Y.1
Monden, A.2
Matsumoto, S.3
Kakimoto, T.4
Matsumoto, K.5
-
26
-
-
77949543086
-
Cost-sensitive learning and the class imbalance problem
-
New York: Springer Sammut C
-
Cost-Sensitive Learning and the Class Imbalance Problem. Ling CX, Sheng VS, Encyclopedia of Machine Learning New York: Springer, Sammut C, 2008
-
(2008)
Encyclopedia of Machine Learning
-
-
Ling, C.X.1
Sheng, V.S.2
-
27
-
-
84888378055
-
-
http://www.seer.cancer.gov
-
Surveillance, Epidemiology, and End Results (SEER) Program, Research Data (1973-2007), National Cancer Institute, DCCPS, Surveillance Research Program, Cancer Statistics Branch, released April 2010, based on the November 2009 submission http://www.seer.cancer.gov
-
Surveillance, Epidemiology, and End Results (SEER) Program, Research Data (1973-2007), National Cancer Institute, DCCPS, Surveillance Research Program, Cancer Statistics Branch, Released April 2010, Based on the November 2009 Submission
-
-
-
28
-
-
84859201426
-
Lung cancer survival prediction using ensemble data mining on SEER data
-
Lung cancer survival prediction using ensemble data mining on SEER data. Agrawal A, Misra S, Narayanan R, Polepeddi L, Choudhary A, Sci Program 2012 20 29 42
-
(2012)
Sci Program
, vol.20
, pp. 29-42
-
-
Agrawal, A.1
Misra, S.2
Narayanan, R.3
Polepeddi, L.4
Choudhary, A.5
-
32
-
-
33745561205
-
An introduction to variable and feature selection
-
An introduction to variable and feature selection. Guyon I, Elisseeff A, J Mach Learn Res 2003 3 1157 1182
-
(2003)
J Mach Learn Res
, vol.3
, pp. 1157-1182
-
-
Guyon, I.1
Elisseeff, A.2
-
36
-
-
76749092270
-
The weka data mining software: An update
-
10.1145/1656274.1656278
-
The WEKA Data Mining Software: An Update. Hall M, Frank E, Holmes G, Pfahringer B, Reutemann P, Witten IH, ACM SIGKDD Explorations Newsletter 2009 11 10 18 10.1145/1656274.1656278
-
(2009)
ACM SIGKDD Explorations Newsletter
, vol.11
, pp. 10-18
-
-
Hall, M.1
Frank, E.2
Holmes, G.3
Pfahringer, B.4
Reutemann, P.5
Witten, I.H.6
-
37
-
-
75149183709
-
Virtual screening of bioassay data
-
10.1186/1758-2946-1-12
-
Virtual screening of bioassay data. Schierz AC, J Cheminformatics 2009 1 12 10.1186/1758-2946-1-12
-
(2009)
J Cheminformatics
, vol.1
, pp. 12
-
-
Schierz, A.C.1
-
39
-
-
0035873821
-
Logistic regression when binary predictor variables are highly correlated
-
DOI 10.1002/sim.680
-
Logistic regression when binary predictor variables are highly correlated. Barker L, Brown C, Stat Med 2001 20 1431 1442 10.1002/sim.680 11343364 (Pubitemid 32433189)
-
(2001)
Statistics in Medicine
, vol.20
, Issue.9-10
, pp. 1431-1442
-
-
Barker, L.1
Brown, C.2
-
42
-
-
84856964446
-
Analysis of preprocessing vs cost-sensitive learning for imbalanced classification. Open problems on intrinsic data characteristics
-
10.1016/j.eswa.2011.12.043
-
Analysis of preprocessing vs. cost-sensitive learning for imbalanced classification. Open problems on intrinsic data characteristics. Lopez V, Fernández A, Moreno-Torres JG, Herrera F, Expert Syst Appl 2012 39 6585 6608 10.1016/j.eswa.2011.12.043
-
(2012)
Expert Syst Appl
, vol.39
, pp. 6585-6608
-
-
Lopez, V.1
Fernández, A.2
Moreno-Torres, J.G.3
Herrera, F.4
-
44
-
-
78649528598
-
Discretization of continuous valued dimensions in OLAP data cubes
-
Discretization of continuous valued dimensions in OLAP data cubes. Palaniappan S, Hong TK, Int J Comput Sci Network Secur 2008 8 116 126
-
(2008)
Int J Comput Sci Network Secur
, vol.8
, pp. 116-126
-
-
Palaniappan, S.1
Hong, T.K.2
-
45
-
-
84888371831
-
Prediction of breast cancer survivability: To alleviate oncologists in decision making
-
Seoul, Korea: Seoul, Korea
-
Prediction of breast cancer survivability: to alleviate oncologists in decision making. Ali A, An Y, Kim D, Park K, Shin H, Kim M, Proceeding of the Business Intelligence and Data Mining Conference Seoul, Korea: Seoul, Korea 2010 80 92
-
(2010)
Proceeding of the Business Intelligence and Data Mining Conference
, pp. 80-92
-
-
Ali, A.1
An, Y.2
Kim, D.3
Park, K.4
Shin, H.5
Kim, M.6
|