메뉴 건너뛰기




Volumn 48, Issue 1, 2015, Pages 276-287

Optimizing area under the ROC curve using semi-supervised learning

Author keywords

AUC; RankBoost; Receiver operating characteristic; Semi supervised learning; Semidefinite programming; SSLROC; SVMROC; Transfer learning

Indexed keywords

CLASSIFICATION (OF INFORMATION); LEARNING SYSTEMS; OPTIMIZATION; TRANSFER LEARNING;

EID: 85027958432     PISSN: 00313203     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patcog.2014.07.025     Document Type: Article
Times cited : (47)

References (54)
  • 1
    • 0031191630 scopus 로고    scopus 로고
    • The use of the area under the ROC curve in the evaluation of machine learning algorithms
    • A.P. Bradley The use of the area under the ROC curve in the evaluation of machine learning algorithms Pattern Recognit. 30 1997 1145 1159
    • (1997) Pattern Recognit. , vol.30 , pp. 1145-1159
    • Bradley, A.P.1
  • 2
    • 0003562954 scopus 로고    scopus 로고
    • A simple generalisation of the area under the ROC curve for multiple class classification problems
    • D.J. Hand, and R.J. Till A simple generalisation of the area under the ROC curve for multiple class classification problems Mach. Learn. 45 2001 171 186
    • (2001) Mach. Learn. , vol.45 , pp. 171-186
    • Hand, D.J.1    Till, R.J.2
  • 7
    • 14644390912 scopus 로고    scopus 로고
    • Using AUC and accuracy in evaluating learning algorithms
    • J. Huang, and C.X. Ling Using AUC and accuracy in evaluating learning algorithms IEEE Trans. Knowl. Data Eng. 17 2005 299 310
    • (2005) IEEE Trans. Knowl. Data Eng. , vol.17 , pp. 299-310
    • Huang, J.1    Ling, C.X.2
  • 9
    • 33846606857 scopus 로고    scopus 로고
    • Optimizing the area under a receiver operating characteristic curve with application to land-mine detection
    • W.H. Lee, P.D. Gader, and J.N. Wilson Optimizing the area under a receiver operating characteristic curve with application to land-mine detection IEEE Trans. Geosci. Remote Sens. 45 2007 389 397
    • (2007) IEEE Trans. Geosci. Remote Sens. , vol.45 , pp. 389-397
    • Lee, W.H.1    Gader, P.D.2    Wilson, J.N.3
  • 10
    • 48349140000 scopus 로고    scopus 로고
    • A critical analysis of variants of the AUC
    • S. Vanderlooy, and E. Hullermeier A critical analysis of variants of the AUC Mach. Learn. 72 2008 247 262
    • (2008) Mach. Learn. , vol.72 , pp. 247-262
    • Vanderlooy, S.1    Hullermeier, E.2
  • 11
    • 48149091075 scopus 로고    scopus 로고
    • Maximizing area under ROC curve for biometric scores fusion
    • K.A. Toh, J. Kim, and S. Lee Maximizing area under ROC curve for biometric scores fusion Pattern Recognit. 41 2008 3373 3392
    • (2008) Pattern Recognit. , vol.41 , pp. 3373-3392
    • Toh, K.A.1    Kim, J.2    Lee, S.3
  • 13
    • 0027457620 scopus 로고
    • Receiver-operating characteristic (ROC) plots - A fundamental evaluation tool in clinical medicine
    • M.H. Zweig, and G. Campbell Receiver-operating characteristic (ROC) plots - a fundamental evaluation tool in clinical medicine Clin. Chem. 39 4 1993 561 577
    • (1993) Clin. Chem. , vol.39 , Issue.4 , pp. 561-577
    • Zweig, M.H.1    Campbell, G.2
  • 15
    • 84855679008 scopus 로고    scopus 로고
    • Caveats and pitfalls of ROC analysis in clinical microarray research (and how to avoid them)
    • D. Berrar, and P. Flach Caveats and pitfalls of ROC analysis in clinical microarray research (and how to avoid them) Brief. Bioinform. 13 1 2012 83 97
    • (2012) Brief. Bioinform. , vol.13 , Issue.1 , pp. 83-97
    • Berrar, D.1    Flach, P.2
  • 16
    • 38949161848 scopus 로고    scopus 로고
    • AUC a misleading measure of the performance of predictive distribution models
    • J.M. Lobo, A. Jiménez-Valverde, and R. Real AUC a misleading measure of the performance of predictive distribution models Glob. Ecol. Biogeogr. 17 2 2007 145 151
    • (2007) Glob. Ecol. Biogeogr. , vol.17 , Issue.2 , pp. 145-151
    • Lobo, J.M.1    Jiménez-Valverde, A.2    Real, R.3
  • 18
    • 39149090650 scopus 로고    scopus 로고
    • Maximizing the area under the ROC curve by pairwise feature combination
    • C. Marrocco, R.P.W. Duin, and F. Tortorella Maximizing the area under the ROC curve by pairwise feature combination Pattern Recognit. 41 6 2008 1961 1974
    • (2008) Pattern Recognit. , vol.41 , Issue.6 , pp. 1961-1974
    • Marrocco, C.1    Duin, R.P.W.2    Tortorella, F.3
  • 19
    • 0033485370 scopus 로고    scopus 로고
    • Ensemble learning via negative correlation
    • Y. Liu, and X. Yao Ensemble learning via negative correlation Neural Netw. 12 10 1999 1399 1404
    • (1999) Neural Netw. , vol.12 , Issue.10 , pp. 1399-1404
    • Liu, Y.1    Yao, X.2
  • 20
    • 0034250160 scopus 로고    scopus 로고
    • An experimental comparison of three methods for constructing ensembles of decision trees bagging, and randomization
    • T.G. Dietterich An experimental comparison of three methods for constructing ensembles of decision trees bagging, and randomization Mach. Learn. 40 2 2000 139 157
    • (2000) Mach. Learn. , vol.40 , Issue.2 , pp. 139-157
    • Dietterich, T.G.1
  • 21
    • 0030211964 scopus 로고    scopus 로고
    • Bagging predictors
    • L. Breiman Bagging predictors Mach. Learn. 24 2 1996 123 140
    • (1996) Mach. Learn. , vol.24 , Issue.2 , pp. 123-140
    • Breiman, L.1
  • 22
    • 84983110889 scopus 로고
    • A decision-theoretic generalization of on-line learning and an application to boosting
    • Springer Berlin/Heidelberg
    • Y. Freund, R.E. Schapire, A decision-theoretic generalization of on-line learning and an application to boosting, in: Computational Learning Theory, vol. 904, Springer Berlin/Heidelberg, 1995, pp. 23-37.
    • (1995) Computational Learning Theory , vol.904 , pp. 23-37
    • Freund, Y.1    Schapire, R.E.2
  • 23
    • 4644367942 scopus 로고    scopus 로고
    • An efficient boosting algorithm for combining preferences
    • Y. Freund, R. Iyer, R. Schapire, and Y. Singer An efficient boosting algorithm for combining preferences J. Mach. Learn. Res. 4 2003 933 969
    • (2003) J. Mach. Learn. Res. , vol.4 , pp. 933-969
    • Freund, Y.1    Iyer, R.2    Schapire, R.3    Singer, Y.4
  • 29
    • 56449113841 scopus 로고    scopus 로고
    • Newton methods for fast solution of semi-supervised linear SVMs
    • MIT Press Cambridge MA, US
    • V. Sindhwani, and S.S. Keerthi Newton methods for fast solution of semi-supervised linear SVMs Large Scale Kernel Machines 2007 MIT Press Cambridge MA, US
    • (2007) Large Scale Kernel Machines
    • Sindhwani, V.1    Keerthi, S.S.2
  • 33
    • 0030106462 scopus 로고    scopus 로고
    • Semidefinite programming
    • L. Vandenberghe, and S. Boyd Semidefinite programming SIAM Rev. 38 1 1996 49 95
    • (1996) SIAM Rev. , vol.38 , Issue.1 , pp. 49-95
    • Vandenberghe, L.1    Boyd, S.2
  • 34
    • 84898958346 scopus 로고    scopus 로고
    • Semi-supervised support vector machines
    • D.A.C. Michael, S. Kearns, Sara A. Solla
    • K. Bennett, and A. Demiriz Semi-supervised support vector machines D.A.C. Michael, S. Kearns, Sara A. Solla, Advances in Neural Information Processing Systems vol. 11 1999 368 374
    • (1999) Advances in Neural Information Processing Systems , vol.11 VOL. , pp. 368-374
    • Bennett, K.1    Demiriz, A.2
  • 35
    • 79955702502 scopus 로고    scopus 로고
    • LIBSVM a library for support vector machines
    • C.-C. Chang, and C.-J. Lin LIBSVM a library for support vector machines ACM Trans. Intell. Syst. Technol. 2 3 2011 27:1 27:27
    • (2011) ACM Trans. Intell. Syst. Technol. , vol.2 , Issue.3 , pp. 271-2727
    • Chang, C.-C.1    Lin, C.-J.2
  • 36
    • 0000259511 scopus 로고    scopus 로고
    • Approximate statistical tests for comparing supervised classification learning algorithms
    • T.G. Dietterich Approximate statistical tests for comparing supervised classification learning algorithms Neural Comput. 10 7 1998 1895 1923
    • (1998) Neural Comput. , vol.10 , Issue.7 , pp. 1895-1923
    • Dietterich, T.G.1
  • 37
    • 3142743708 scopus 로고    scopus 로고
    • Solving semidefinite-quadratic-linear programs using SDPT3
    • R.H. Tutuncu, K.C. Toh, and M.J. Todd Solving semidefinite-quadratic-linear programs using SDPT3 Math. Progr. 95 2 2003 189 217
    • (2003) Math. Progr. , vol.95 , Issue.2 , pp. 189-217
    • Tutuncu, R.H.1    Toh, K.C.2    Todd, M.J.3
  • 41
    • 78649934709 scopus 로고    scopus 로고
    • University of California, School of Information and Computer Science, Irvine, CA
    • A. Frank, A. Asuncion, UCI machine learning repository. 〈 http://archive.ics.uci.edu/ml 〉, University of California, School of Information and Computer Science, Irvine, CA.
    • UCI Machine Learning Repository
    • Frank, A.1    Asuncion, A.2
  • 42
    • 0019362251 scopus 로고
    • Introduction to sample-size determination and power analysis for clinical-trials
    • J.M. Lachin Introduction to sample-size determination and power analysis for clinical-trials Control. Clin. Trials 2 2 1981 93 113
    • (1981) Control. Clin. Trials , vol.2 , Issue.2 , pp. 93-113
    • Lachin, J.M.1
  • 43
    • 33645762226 scopus 로고
    • A sharper Bonferroni procedure for multiple tests of significance
    • Y. Hochberg A sharper Bonferroni procedure for multiple tests of significance Biometrika 75 4 1988 800 802
    • (1988) Biometrika , vol.75 , Issue.4 , pp. 800-802
    • Hochberg, Y.1
  • 46
    • 77952572733 scopus 로고    scopus 로고
    • Improving the accuracy of CT colonography interpretation: Computer-aided diagnosis
    • R.M. Summers Improving the accuracy of CT colonography interpretation: computer-aided diagnosis Gastrointest. Endosc. Clin. N. Am. 20 2010 245 257
    • (2010) Gastrointest. Endosc. Clin. N. Am. , vol.20 , pp. 245-257
    • Summers, R.M.1
  • 47
    • 77951683262 scopus 로고    scopus 로고
    • Combining statistical and geometric features for colonic polyp detection in CTC based on multiple kernel learning
    • S. Wang, J. Yao, N. Petrick, and R.M. Summers Combining statistical and geometric features for colonic polyp detection in CTC based on multiple kernel learning Int. J. Comput. Intell. Appl. 9 1 2010 1 15
    • (2010) Int. J. Comput. Intell. Appl. , vol.9 , Issue.1 , pp. 1-15
    • Wang, S.1    Yao, J.2    Petrick, N.3    Summers, R.M.4
  • 48
    • 78149266353 scopus 로고    scopus 로고
    • Massive-training artificial neural network coupled with Laplacian-eigenfunction-based dimensionality reduction for computer-aided detection of polyps in CT colonography
    • K. Suzuki, J. Zhang, and J.W. Xu Massive-training artificial neural network coupled with Laplacian-eigenfunction-based dimensionality reduction for computer-aided detection of polyps in CT colonography IEEE Trans. Med. Imag. 29 11 2010 1907 1917
    • (2010) IEEE Trans. Med. Imag. , vol.29 , Issue.11 , pp. 1907-1917
    • Suzuki, K.1    Zhang, J.2    Xu, J.W.3
  • 49
    • 84860658409 scopus 로고    scopus 로고
    • Seeing is believing video classification for computed tomographic colonography using multiple-instance learning
    • S. Wang, M.T. McKenna, T.B. Nguyen, J.E. Burns, N. Petrick, B. Sahiner, and R.M. Summers Seeing is believing video classification for computed tomographic colonography using multiple-instance learning IEEE Trans. Med. Imag. 31 5 2012 1141 1153
    • (2012) IEEE Trans. Med. Imag. , vol.31 , Issue.5 , pp. 1141-1153
    • Wang, S.1    McKenna, M.T.2    Nguyen, T.B.3    Burns, J.E.4    Petrick, N.5    Sahiner, B.6    Summers, R.M.7
  • 50
    • 33644621419 scopus 로고    scopus 로고
    • Hybrid segmentation of colon filled with air and opacified fluid for CT colonography
    • M. Franaszek, R.M. Summers, P.J. Pickhardt, and J.R. Choi Hybrid segmentation of colon filled with air and opacified fluid for CT colonography IEEE Trans. Med. Imag. 25 3 2006 358 368
    • (2006) IEEE Trans. Med. Imag. , vol.25 , Issue.3 , pp. 358-368
    • Franaszek, M.1    Summers, R.M.2    Pickhardt, P.J.3    Choi, J.R.4
  • 51
    • 17044407594 scopus 로고    scopus 로고
    • Support vector machines committee classification method for computer-aided polyp detection in CT colonography
    • A.K. Jerebko, J.D. Malley, M. Franaszek, and R.M. Summers Support vector machines committee classification method for computer-aided polyp detection in CT colonography Acad. Radiol. 12 4 2005 479 486
    • (2005) Acad. Radiol. , vol.12 , Issue.4 , pp. 479-486
    • Jerebko, A.K.1    Malley, J.D.2    Franaszek, M.3    Summers, R.M.4


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.