메뉴 건너뛰기




Volumn , Issue , 2010, Pages 173-181

Active learning for biomedical citation screening

Author keywords

Active learning; Applications; Medical; Text classification

Indexed keywords

ACTIVE LEARNING; BIOMEDICAL CITATIONS; CLASSIFICATION PERFORMANCE; DATA SETS; DOMAIN EXPERTS; DOMAIN KNOWLEDGE; EMPIRICAL RESULTS; EVALUATION FRAMEWORK; EVIDENCE-BASED PRACTICES; FALSE NEGATIVES; FALSE POSITIVE; LABELED DATA; LINEAR PROGRAMMING ALGORITHM; MEDICAL; MEDICAL CENTER; MEDICAL DECISIONS; PREDICTIVE MODELS; REAL-WORLD; REAL-WORLD APPLICATION; SYSTEMATIC REVIEW; TEXT CLASSIFICATION; UTILITY MEASURE;

EID: 77956197516     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1835804.1835829     Document Type: Conference Paper
Times cited : (96)

References (31)
  • 1
    • 21844444960 scopus 로고    scopus 로고
    • Online choice of active learning algorithms
    • Y. Baram, R. El-Yaniv, and K. Luz. Online choice of active learning algorithms. J. Mach. Learn. Res., 5:255-291, 2004.
    • (2004) J. Mach. Learn. Res. , vol.5 , pp. 255-291
    • Baram, Y.1    El-Yaniv, R.2    Luz, K.3
  • 2
    • 0031620208 scopus 로고    scopus 로고
    • Combining labeled and unlabeled data with co-training
    • A. Blum and T. Mitchell. Combining labeled and unlabeled data with co-training. In COLT, pages 92-100, 1998.
    • (1998) COLT , pp. 92-100
    • Blum, A.1    Mitchell, T.2
  • 4
    • 0030854892 scopus 로고    scopus 로고
    • Formulating questions and locating primary studies for inclusion in systematic reviews
    • Sep
    • C. Counsell. Formulating questions and locating primary studies for inclusion in systematic reviews. Ann. Intern. Med., 127:380-387, Sep 1997.
    • (1997) Ann. Intern. Med. , vol.127 , pp. 380-387
    • Counsell, C.1
  • 5
    • 70349254685 scopus 로고    scopus 로고
    • Proactive learning: Cost-sensitive active learning with multiple imperfect oracles
    • P. Donmez and J. G. Carbonell. Proactive learning: cost-sensitive active learning with multiple imperfect oracles. In CIKM, pages 619-628, 2008.
    • (2008) CIKM , pp. 619-628
    • Donmez, P.1    Carbonell, J.G.2
  • 6
    • 57349122015 scopus 로고    scopus 로고
    • Learning from labeled features using generalized expectation criteria
    • G. Druck, G. S. Mann, and A. McCallum. Learning from labeled features using generalized expectation criteria. In SIGIR, pages 595-602, 2009.
    • (2009) SIGIR , pp. 595-602
    • Druck, G.1    Mann, G.S.2    McCallum, A.3
  • 7
    • 77949519575 scopus 로고    scopus 로고
    • Active learning by labeling features
    • G. Druck, B. Settles, and A. McCallum. Active learning by labeling features. In EMNLP, pages 81-90, 2009.
    • (2009) EMNLP , pp. 81-90
    • Druck, G.1    Settles, B.2    McCallum, A.3
  • 8
    • 0031209604 scopus 로고    scopus 로고
    • Selective sampling using the query by committee algorithm
    • Y. Freund, H. S. Seung, E. Shamir, and N. Tishby. Selective sampling using the query by committee algorithm. In Machine Learning, volume 28, pages 133-168, 1997.
    • (1997) Machine Learning , vol.28 , pp. 133-168
    • Freund, Y.1    Seung, H.S.2    Shamir, E.3    Tishby, N.4
  • 10
    • 33344465511 scopus 로고    scopus 로고
    • Biomedical language processing: What's beyond pubmed?
    • March
    • L. Hunter and K. B. Cohen. Biomedical language processing: What's beyond pubmed? Mol Cell, 21(5):589-594, March 2006.
    • (2006) Mol Cell , vol.21 , Issue.5 , pp. 589-594
    • Hunter, L.1    Cohen, K.B.2
  • 12
    • 84957069814 scopus 로고    scopus 로고
    • Text categorization with support vector machines: Learning with many relevant features
    • T. Joachims. Text categorization with support vector machines: Learning with many relevant features. In Machine Learning: ECML-98, pages 137-142, 1998.
    • (1998) Machine Learning: ECML-98 , pp. 137-142
    • Joachims, T.1
  • 13
    • 0029193061 scopus 로고
    • Evaluating and optimizing autonomous text classification systems
    • D. Lewis. Evaluating and optimizing autonomous text classification systems. In SIGIR, pages 246-254, 1995.
    • (1995) SIGIR , pp. 246-254
    • Lewis, D.1
  • 14
    • 85013879626 scopus 로고
    • A sequential algorithm for training text classifiers
    • D. Lewis and W. Gale. A sequential algorithm for training text classifiers. In SIGIR, pages 3-12, 1994.
    • (1994) SIGIR , pp. 3-12
    • Lewis, D.1    Gale, W.2
  • 15
    • 0000314722 scopus 로고    scopus 로고
    • Employing EM and pool-based active learning for text classification
    • San Francisco, CA, USA
    • A. Mccallum and K. Nigam. Employing EM and pool-based active learning for text classification. In ICML, pages 350-358, San Francisco, CA, USA, 1998.
    • (1998) ICML , pp. 350-358
    • Mccallum, A.1    Nigam, K.2
  • 17
    • 0000684645 scopus 로고
    • Breast cancer diagnosis and prognosis via linear programming
    • W. N. S. O. L. Mangasarian and W. W. Wolberg. Breast cancer diagnosis and prognosis via linear programming. Operations Research, 43:570-577, 1995.
    • (1995) Operations Research , vol.43 , pp. 570-577
    • Mangasarian, W.N.S.O.L.1    Wolberg, W.W.2
  • 18
    • 33750360046 scopus 로고    scopus 로고
    • Balancing exploration and exploitation: A new algorithm for active machine learning
    • T. Osugi, D. Kun, and S. Scott. Balancing exploration and exploitation: A new algorithm for active machine learning. In ICDM, pages 330-337, 2005.
    • (2005) ICDM , pp. 330-337
    • Osugi, T.1    Kun, D.2    Scott, S.3
  • 19
    • 36448950134 scopus 로고    scopus 로고
    • An interactive algorithm for asking and incorporating feature feedback into support vector machines
    • H. Raghavan and J. Allan. An interactive algorithm for asking and incorporating feature feedback into support vector machines. In SIGIR, pages 79-86, 2007.
    • (2007) SIGIR , pp. 79-86
    • Raghavan, H.1    Allan, J.2
  • 20
    • 33747134006 scopus 로고    scopus 로고
    • Active learning with feedback on features and instances
    • H. Raghavan, O. Madani, and R. Jones. Active learning with feedback on features and instances. J. Mach. Learn. Res., 7:1655-1686, 2006.
    • (2006) J. Mach. Learn. Res. , vol.7 , pp. 1655-1686
    • Raghavan, H.1    Madani, O.2    Jones, R.3
  • 21
    • 34547628187 scopus 로고    scopus 로고
    • Performance thresholding in practical text classification
    • H. New York, NY, USA
    • V. E. Schütze, H. and J. Pedersen. Performance thresholding in practical text classification. In CIKM, pages 662-671, New York, NY, USA, 2006.
    • (2006) CIKM , pp. 662-671
    • Schütze, V.E.1    Pedersen, J.2
  • 22
    • 68949137209 scopus 로고    scopus 로고
    • Active learning literature survey
    • University of Wisconsin-Madison
    • B. Settles. Active learning literature survey. Technical Report 1648, University of Wisconsin-Madison, 2009.
    • (2009) Technical Report 1648
    • Settles, B.1
  • 23
    • 65449144451 scopus 로고    scopus 로고
    • Get another label? Improving data quality and data mining using multiple, noisy labelers
    • V. S. Sheng, F. Provost, and P. G. Ipeirotis. Get another label? improving data quality and data mining using multiple, noisy labelers. In KDD, pages 614-622, 2008.
    • (2008) KDD , pp. 614-622
    • Sheng, V.S.1    Provost, F.2    Ipeirotis, P.G.3
  • 24
    • 71149105884 scopus 로고    scopus 로고
    • Uncertainty sampling and transductive experimental design for active dual supervision
    • V. Sindhwani, P. Melville, and R. D. Lawrence. Uncertainty sampling and transductive experimental design for active dual supervision. In ICML, pages 120-128, 2009.
    • (2009) ICML , pp. 120-128
    • Sindhwani, V.1    Melville, P.2    Lawrence, R.D.3
  • 25
    • 77956208474 scopus 로고    scopus 로고
    • A web survey on the use of active learning to support annotation of text data
    • June
    • K. Tomanek and F. Olsson. A web survey on the use of active learning to support annotation of text data. In NAACL Workshop on AL for NLP, pages 45-48, June 2009.
    • (2009) NAACL Workshop on AL for NLP , pp. 45-48
    • Tomanek, K.1    Olsson, F.2
  • 26
    • 0042868698 scopus 로고    scopus 로고
    • Support vector machine active learning with applications to text classification
    • S. Tong and D. Koller. Support vector machine active learning with applications to text classification. In J. Mach. Learn. Res., pages 999-1006, 2000.
    • (2000) J. Mach. Learn. Res. , pp. 999-1006
    • Tong, S.1    Koller, D.2
  • 28
    • 33750905259 scopus 로고    scopus 로고
    • Decision curve analysis: A novel method for evaluating prediction models
    • A. J. Vickers and E. B. Elkin. Decision curve analysis: A novel method for evaluating prediction models. Medical Decision Making, 26:565-574, 2006.
    • (2006) Medical Decision Making , vol.26 , pp. 565-574
    • Vickers, A.J.1    Elkin, E.B.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.