메뉴 건너뛰기




Volumn 51, Issue , 2014, Pages 165-175

Multi-label classification of chronically ill patients with bag of words and supervised dimensionality reduction algorithms

Author keywords

Clinical data; Complex patient; Diabetes type 2; Dimensionality reduction; Kernel methods; Multi label classification

Indexed keywords

CLINICAL RESEARCH; DATA REDUCTION; DECISION SUPPORT SYSTEMS; DIMENSIONALITY REDUCTION; ELECTROLYTES; PULMONARY DISEASES; TIME SERIES;

EID: 84908027298     PISSN: 15320464     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jbi.2014.05.010     Document Type: Article
Times cited : (46)

References (41)
  • 1
    • 84905661637 scopus 로고    scopus 로고
    • Economic implications of type 2 diabetes management
    • McAdam Marx C. Economic implications of type 2 diabetes management. Am J Manag Care 2013, 19:143-148.
    • (2013) Am J Manag Care , vol.19 , pp. 143-148
    • McAdam Marx, C.1
  • 3
    • 84875251123 scopus 로고    scopus 로고
    • Understanding the nature of information seeking behavior in critical care: Implications for the design of health information technology
    • Kannampallil T.G., Franklin A., Mishra R., Almoosa K.F., Cohen T., Patel V.L. Understanding the nature of information seeking behavior in critical care: Implications for the design of health information technology. Artif Intell Med 2013, 57:21-29.
    • (2013) Artif Intell Med , vol.57 , pp. 21-29
    • Kannampallil, T.G.1    Franklin, A.2    Mishra, R.3    Almoosa, K.F.4    Cohen, T.5    Patel, V.L.6
  • 5
    • 84879940846 scopus 로고    scopus 로고
    • Supervised patient similarity measure of heterogeneous patient records
    • Sun J., Wang F., Hu J., Edabollahi S. Supervised patient similarity measure of heterogeneous patient records. SIGKDD Explor 2012, 14:16-24.
    • (2012) SIGKDD Explor , vol.14 , pp. 16-24
    • Sun, J.1    Wang, F.2    Hu, J.3    Edabollahi, S.4
  • 6
    • 77958396896 scopus 로고
    • Nearest neighbour searches and the curse of dimensionality
    • Marimont R.B., Shapiro M.B. Nearest neighbour searches and the curse of dimensionality. IMA J Appl Math 1979, 24:59-70.
    • (1979) IMA J Appl Math , vol.24 , pp. 59-70
    • Marimont, R.B.1    Shapiro, M.B.2
  • 7
    • 84908022891 scopus 로고    scopus 로고
    • Bag-of-words representation for biomedical time series classification
    • CoRR abs/1212.2262
    • Wang J, Liu P, She MF, Nahavandi S, Kouzani AZ. Bag-of-words representation for biomedical time series classification. CoRR abs/1212.2262; 2012.
    • (2012)
    • Wang, J.1    Liu, P.2    She, M.F.3    Nahavandi, S.4    Kouzani, A.Z.5
  • 8
    • 84870466073 scopus 로고    scopus 로고
    • Supervised learning and codebook optimization for bag of words models
    • Jiu M., Wolf C., Garcia C., Baskurt A. Supervised learning and codebook optimization for bag of words models. Cognitive Comput 2012, 4:409-419.
    • (2012) Cognitive Comput , vol.4 , pp. 409-419
    • Jiu, M.1    Wolf, C.2    Garcia, C.3    Baskurt, A.4
  • 9
    • 84857163395 scopus 로고    scopus 로고
    • Using modified multivariate bag-of-words models to classify physiological data
    • Spiliopoulou M, Wang H, Cook DJ, Pei J, Wang W, Zaïane OR, et al. editors.
    • Ordóñez P, Armstrong T, Oates T, Fackler J. Using modified multivariate bag-of-words models to classify physiological data. In: Spiliopoulou M, Wang H, Cook DJ, Pei J, Wang W, Zaïane OR, et al. editors. ICDM workshops. IEEE; 2011. p. 534-39.
    • (2011) ICDM workshops. IEEE , pp. 534-539
    • Ordóñez, P.1    Armstrong, T.2    Oates, T.3    Fackler, J.4
  • 10
    • 84861617363 scopus 로고    scopus 로고
    • An extensive experimental comparison of methods for multi-label learning
    • Madjarov G., Kocev D., Gjorgjevikj D., Dzeroski S. An extensive experimental comparison of methods for multi-label learning. Pattern Recognit 2012, 45:3084-3104.
    • (2012) Pattern Recognit , vol.45 , pp. 3084-3104
    • Madjarov, G.1    Kocev, D.2    Gjorgjevikj, D.3    Dzeroski, S.4
  • 13
    • 84867671576 scopus 로고    scopus 로고
    • Improving multi-label classification using semi-supervised learning and dimensionality reduction
    • Springer, P. Anthony, M. Ishizuka, D. Lukose (Eds.) PRICAI
    • Pacharawongsakda E., Nattee C., Theeramunkong T. Improving multi-label classification using semi-supervised learning and dimensionality reduction. Lecture notes in computer science 2012, vol. 7458:423-434. Springer. P. Anthony, M. Ishizuka, D. Lukose (Eds.).
    • (2012) Lecture notes in computer science , vol.7458 , pp. 423-434
    • Pacharawongsakda, E.1    Nattee, C.2    Theeramunkong, T.3
  • 14
    • 79960318399 scopus 로고    scopus 로고
    • Semi-supervised dimension reduction for multi-label classification
    • AAAI Press, M. Fox, D. Poole (Eds.)
    • Qian B., Davidson I. Semi-supervised dimension reduction for multi-label classification. AAAI 2010, AAAI Press. M. Fox, D. Poole (Eds.).
    • (2010) AAAI
    • Qian, B.1    Davidson, I.2
  • 16
    • 55849133043 scopus 로고    scopus 로고
    • Diabetes-related microvascular and macrovascular diseases in the physical therapy setting
    • Cade W.T. Diabetes-related microvascular and macrovascular diseases in the physical therapy setting. Phys Ther 2008, 88:1322-1335.
    • (2008) Phys Ther , vol.88 , pp. 1322-1335
    • Cade, W.T.1
  • 17
    • 79955479858 scopus 로고    scopus 로고
    • Multiparameter intelligent monitoring in intensive care II (MIMIC-II): a public-access intensive care unit database
    • Saeed M., Villarroel M., Reisner A.T., Clifford G., Lehman L.-W., Moody G., et al. Multiparameter intelligent monitoring in intensive care II (MIMIC-II): a public-access intensive care unit database. Crit Care Med 2011, 39:952-960.
    • (2011) Crit Care Med , vol.39 , pp. 952-960
    • Saeed, M.1    Villarroel, M.2    Reisner, A.T.3    Clifford, G.4    Lehman, L.-W.5    Moody, G.6
  • 18
    • 33947681316 scopus 로고    scopus 로고
    • ML-KNN: a lazy learning approach to multi-label learning
    • Zhang M.-L., Zhou Z.-H. ML-KNN: a lazy learning approach to multi-label learning. Pattern Recognit 2007, 40:2038-2048.
    • (2007) Pattern Recognit , vol.40 , pp. 2038-2048
    • Zhang, M.-L.1    Zhou, Z.-H.2
  • 20
    • 0345134164 scopus 로고    scopus 로고
    • Histogram intersection kernel for image classification
    • Barla A, Odone F, Verri A. Histogram intersection kernel for image classification. In: ICIP (3). p. 513-16.
    • ICIP , Issue.3 , pp. 513-516
    • Barla, A.1    Odone, F.2    Verri, A.3
  • 21
    • 13444286179 scopus 로고    scopus 로고
    • Locality preserving projections
    • MIT Press, Cambridge, MA, S. Thrun, L. Saul, B. Schölkopf (Eds.)
    • He X., Niyogi P. Locality preserving projections. Advances in neural information processing systems, vol. 16 2004, MIT Press, Cambridge, MA. S. Thrun, L. Saul, B. Schölkopf (Eds.).
    • (2004) Advances in neural information processing systems, vol. 16
    • He, X.1    Niyogi, P.2
  • 23
    • 34249086815 scopus 로고    scopus 로고
    • Dimensionality reduction of multimodal labeled data by local fisher discriminant analysis
    • Sugiyama M. Dimensionality reduction of multimodal labeled data by local fisher discriminant analysis. J Mach Learning Res 2007, 8:1027-1061.
    • (2007) J Mach Learning Res , vol.8 , pp. 1027-1061
    • Sugiyama, M.1
  • 25
    • 84884486714 scopus 로고    scopus 로고
    • Strategies for dealing with missing data in clinical trials: from design to analysis
    • Dziura J.D., Post L.A., Zhao Q., Fu Z., Peduzzi P. Strategies for dealing with missing data in clinical trials: from design to analysis. Yale J Biol Med 2013, 86:343-358.
    • (2013) Yale J Biol Med , vol.86 , pp. 343-358
    • Dziura, J.D.1    Post, L.A.2    Zhao, Q.3    Fu, Z.4    Peduzzi, P.5
  • 26
    • 77954801383 scopus 로고    scopus 로고
    • How much can we learn about missing data? An exploration of a clinical trial in psychiatry
    • Jackson D., White I.R., Leese M. How much can we learn about missing data? An exploration of a clinical trial in psychiatry. J R Stat Soc Ser A Stat Soc 2010, 173:593-612.
    • (2010) J R Stat Soc Ser A Stat Soc , vol.173 , pp. 593-612
    • Jackson, D.1    White, I.R.2    Leese, M.3
  • 27
    • 0000764772 scopus 로고
    • The use of multiple measurements in taxonomic problems
    • Fisher R.A. The use of multiple measurements in taxonomic problems. Ann Eugenics 1936, 7:179-188.
    • (1936) Ann Eugenics , vol.7 , pp. 179-188
    • Fisher, R.A.1
  • 28
    • 79959252247 scopus 로고    scopus 로고
    • A dependent multilabel classification method derived from the k-nearest neighbor rule
    • Younes Z., Abdallah F., Denoeux T., Snoussi H. A dependent multilabel classification method derived from the k-nearest neighbor rule. EURASIP J Adv Sig Proc 2011, 2011.
    • (2011) EURASIP J Adv Sig Proc , vol.2011
    • Younes, Z.1    Abdallah, F.2    Denoeux, T.3    Snoussi, H.4
  • 29
    • 33748366796 scopus 로고    scopus 로고
    • Multilabel neural networks with applications to functional genomics and text categorization
    • Zhang M.-L., Zhou Z.-H. Multilabel neural networks with applications to functional genomics and text categorization. IEEE Trans Knowl Data Eng 2006, 18:1338-1351.
    • (2006) IEEE Trans Knowl Data Eng , vol.18 , pp. 1338-1351
    • Zhang, M.-L.1    Zhou, Z.-H.2
  • 30
    • 34249753618 scopus 로고
    • Support-vector networks
    • Cortes C., Vapnik V. Support-vector networks. Mach Learning 1995, 20:273-297.
    • (1995) Mach Learning , vol.20 , pp. 273-297
    • Cortes, C.1    Vapnik, V.2
  • 32
    • 62249222499 scopus 로고    scopus 로고
    • Efficient visual search of videos cast as text retrieval
    • Sivic J., Zisserman A. Efficient visual search of videos cast as text retrieval. IEEE Trans Pattern Anal Mach Intell 2009, 31:591-606.
    • (2009) IEEE Trans Pattern Anal Mach Intell , vol.31 , pp. 591-606
    • Sivic, J.1    Zisserman, A.2
  • 33
    • 35048835661 scopus 로고    scopus 로고
    • Efficient visual content retrieval and mining in videos
    • Springer, Berlin Heidelberg, K. Aizawa, Y. Nakamura, S. Satoh (Eds.) Advances in multimedia information processing - PCM 2004
    • Sivic J., Zisserman A. Efficient visual content retrieval and mining in videos. Lecture notes in computer science 2005, vol. 3332:471-478. Springer, Berlin Heidelberg. K. Aizawa, Y. Nakamura, S. Satoh (Eds.).
    • (2005) Lecture notes in computer science , vol.3332 , pp. 471-478
    • Sivic, J.1    Zisserman, A.2
  • 36
    • 0033905095 scopus 로고    scopus 로고
    • Boostexter: a boosting-based system for text categorization
    • Schapire R.E., Singer Y. Boostexter: a boosting-based system for text categorization. Mach Learning 2000, 39:135-168.
    • (2000) Mach Learning , vol.39 , pp. 135-168
    • Schapire, R.E.1    Singer, Y.2
  • 37
    • 77951132171 scopus 로고    scopus 로고
    • A methodology for training and validating a CAD system and potential pitfalls
    • [CARS 2004 - Computer Assisted Radiology and Surgery. Proceedings of the 18th International Congress and Exhibition]
    • Dundar M., Fung G., Bogoni L., Macari M., Megibow A., Rao B. A methodology for training and validating a CAD system and potential pitfalls. Int Congr Ser 2004, 1268:1010-1014. [CARS 2004 - Computer Assisted Radiology and Surgery. Proceedings of the 18th International Congress and Exhibition].
    • (2004) Int Congr Ser , vol.1268 , pp. 1010-1014
    • Dundar, M.1    Fung, G.2    Bogoni, L.3    Macari, M.4    Megibow, A.5    Rao, B.6
  • 38
    • 63249112814 scopus 로고
    • Dimensionality and sample size considerations
    • In: Krishnaiah P, Kanal L. editors.
    • Jain A, Chandrasekaran B. Dimensionality and sample size considerations. In: Krishnaiah P, Kanal L. editors. Pattern recognition in practice; 1982. p. 835-55.
    • (1982) Pattern recognition in practice , pp. 835-855
    • Jain, A.1    Chandrasekaran, B.2
  • 39
    • 0023987733 scopus 로고
    • On the sensitivity of linear discriminant analysis to sampling variation and analytical errors
    • Linnet K. On the sensitivity of linear discriminant analysis to sampling variation and analytical errors. Comput Biomed Res 1988, 21:158-168.
    • (1988) Comput Biomed Res , vol.21 , pp. 158-168
    • Linnet, K.1
  • 40
    • 84875259046 scopus 로고    scopus 로고
    • Multiple kernel local Fisher discriminant analysis for face recognition
    • [Special issue on Machine Learning in Intelligent Image Processing]
    • Wang Z., Sun X. Multiple kernel local Fisher discriminant analysis for face recognition. Signal Process 2013, 93:1496-1509. [Special issue on Machine Learning in Intelligent Image Processing].
    • (2013) Signal Process , vol.93 , pp. 1496-1509
    • Wang, Z.1    Sun, X.2
  • 41
    • 40849120440 scopus 로고    scopus 로고
    • On applying linear discriminant analysis for multi-labeled problems
    • Park C.H., Lee M. On applying linear discriminant analysis for multi-labeled problems. Pattern Recognit Lett 2008, 29:878-887.
    • (2008) Pattern Recognit Lett , vol.29 , pp. 878-887
    • Park, C.H.1    Lee, M.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.