메뉴 건너뛰기




Volumn 30, Issue 6, 2016, Pages 590-609

On the Influence of Class Noise in Medical Data Classification: Treatment Using Noise Filtering Methods

Author keywords

[No Author keywords available]

Indexed keywords

DECISION SUPPORT SYSTEMS; SPURIOUS SIGNAL NOISE;

EID: 84979295777     PISSN: 08839514     EISSN: 10876545     Source Type: Journal    
DOI: 10.1080/08839514.2016.1193719     Document Type: Article
Times cited : (37)

References (37)
  • 1
    • 84925289085 scopus 로고    scopus 로고
    • Dimensionality reduction of medical big data using neural-fuzzy classifier
    • A.T.Azar,, and A.E.Hassanien. 2015. Dimensionality reduction of medical big data using neural-fuzzy classifier. Soft Computing 19:1115–1127.
    • (2015) Soft Computing , vol.19 , pp. 1115-1127
    • Azar, A.T.1    Hassanien, A.E.2
  • 3
    • 85181549964 scopus 로고
    • In Proceedings of the twelfth international conference on machine learning. Morgan Kaufmann
    • W.W.Cohen, 1995. Fast effective rule induction. In Proceedings of the twelfth international conference on machine learning. Morgan Kaufmann.
    • (1995) Fast effective rule induction
    • Cohen, W.W.1
  • 4
    • 38249042848 scopus 로고
    • On the editing rate of the MULTIEDIT algorithm
    • P.Devijver, 1986. On the editing rate of the MULTIEDIT algorithm. Pattern Recognition Letters 4:9–12.
    • (1986) Pattern Recognition Letters , vol.4 , pp. 9-12
    • Devijver, P.1
  • 5
    • 21144459575 scopus 로고
    • On a monotonicity problem in step-down multiple test procedures
    • H.Finner, 1993. On a monotonicity problem in step-down multiple test procedures. Journal of the American Statistical Association 88:920–923.
    • (1993) Journal of the American Statistical Association , vol.88 , pp. 920-923
    • Finner, H.1
  • 9
    • 0034143132 scopus 로고    scopus 로고
    • Noise detection and elimination in data preprocessing: Experiments in medical domains
    • D.Gamberger,, N.Lavrac, and S.Dzeroski. 2000. Noise detection and elimination in data preprocessing: Experiments in medical domains. Applied Artificial Intelligence 14:205–223.
    • (2000) Applied Artificial Intelligence , vol.14 , pp. 205-223
    • Gamberger, D.1    Lavrac, N.2    Dzeroski, S.3
  • 10
    • 84927970970 scopus 로고    scopus 로고
    • Effect of label noise in the complexity of classification problems
    • L.P.F.Garcia,, A.C.P.L.F.de Carvalho, and A.C.Lorena. 2015. Effect of label noise in the complexity of classification problems. Neurocomputing 160:108–119.
    • (2015) Neurocomputing , vol.160 , pp. 108-119
    • Garcia, L.P.F.1    de Carvalho, A.C.P.L.F.2    Lorena, A.C.3
  • 11
    • 77549084648 scopus 로고    scopus 로고
    • Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: Experimental analysis of power
    • S.García,, A.Fernández, J.Luengo, and F.Herrera. 2010. Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: Experimental analysis of power. Information Sciences 180:2044–2064.
    • (2010) Information Sciences , vol.180 , pp. 2044-2064
    • García, S.1    Fernández, A.2    Luengo, J.3    Herrera, F.4
  • 12
    • 0030125436 scopus 로고    scopus 로고
    • Noise modelling and evaluating learning from examples
    • R.J.Hickey, 1996. Noise modelling and evaluating learning from examples. Artificial Intelligence 82:157–179.
    • (1996) Artificial Intelligence , vol.82 , pp. 157-179
    • Hickey, R.J.1
  • 14
    • 0034922742 scopus 로고    scopus 로고
    • Machine learning for medical diagnosis: History, state of the art and perspective
    • I.Kononenko, 2001. Machine learning for medical diagnosis: History, state of the art and perspective. Artificial Intelligence in Medicine 23:89–109.
    • (2001) Artificial Intelligence in Medicine , vol.23 , pp. 89-109
    • Kononenko, I.1
  • 15
    • 84899493558 scopus 로고    scopus 로고
    • Cytological image analysis with firefly nuclei detection and hybrid one-class classification decomposition
    • B.Krawczyk,, and P.Filipczuk. 2014. Cytological image analysis with firefly nuclei detection and hybrid one-class classification decomposition. Engineering Applications of Artificial Intelligence 31:126–135.
    • (2014) Engineering Applications of Artificial Intelligence , vol.31 , pp. 126-135
    • Krawczyk, B.1    Filipczuk, P.2
  • 16
    • 84901587069 scopus 로고    scopus 로고
    • A hybrid classifier committee for analysing asymmetry features in breast thermograms
    • B.Krawczyk,, and G.Schaefer. 2014. A hybrid classifier committee for analysing asymmetry features in breast thermograms. Applied Soft Computing 20:112–118.
    • (2014) Applied Soft Computing , vol.20 , pp. 112-118
    • Krawczyk, B.1    Schaefer, G.2
  • 17
    • 84906309048 scopus 로고    scopus 로고
    • Hypertension type classification using hierarchical ensemble of one-class classifiers for imbalanced data
    • Bogdanova A.M., Gjorgjevikj D., (eds), Advances in Intelligent Systems and Computing, Switzerland: Springer International
    • B.Krawczyk,, and M.Woźniak. 2015. Hypertension type classification using hierarchical ensemble of one-class classifiers for imbalanced data. In ICT innovations 2014, ed. A.M.Bogdanova and D.Gjorgjevikj, 341–349, Advances in Intelligent Systems and Computing 311, Switzerland: Springer International.
    • (2015) ICT innovations 2014 , pp. 341-349
    • Krawczyk, B.1    Woźniak, M.2
  • 20
    • 33748659204 scopus 로고    scopus 로고
    • Detecting potential labeling errors in microarrays by data perturbation
    • A.Malossini,, E.Blanzieri, and R.T.Ng. 2006. Detecting potential labeling errors in microarrays by data perturbation. Bioinformatics 22:2114–2121.
    • (2006) Bioinformatics , vol.22 , pp. 2114-2121
    • Malossini, A.1    Blanzieri, E.2    Ng, R.T.3
  • 22
    • 84892889305 scopus 로고    scopus 로고
    • Knowledge discovery in clinical decision support systems for pain management: A systematic review
    • N.Pombo,, P.Araújo, and J.Viana. 2014. Knowledge discovery in clinical decision support systems for pain management: A systematic review. Artificial Intelligence in Medicine 60:1–11.
    • (2014) Artificial Intelligence in Medicine , vol.60 , pp. 1-11
    • Pombo, N.1    Araújo, P.2    Viana, J.3
  • 23
    • 33744584654 scopus 로고
    • Induction of decision trees
    • J.R.Quinlan, 1986. Induction of decision trees. Machine Learning 1:81–106.
    • (1986) Machine Learning , vol.1 , pp. 81-106
    • Quinlan, J.R.1
  • 25
    • 84880920627 scopus 로고    scopus 로고
    • Tackling the problem of classification with noisy data using multiple classifier systems: Analysis of the performance and robustness
    • J.A.Sáez,, M.Galar, J.Luengo, and F.Herrera. 2013. Tackling the problem of classification with noisy data using multiple classifier systems: Analysis of the performance and robustness. Information Sciences 247:1–20.
    • (2013) Information Sciences , vol.247 , pp. 1-20
    • Sáez, J.A.1    Galar, M.2    Luengo, J.3    Herrera, F.4
  • 26
    • 84884965072 scopus 로고    scopus 로고
    • Analyzing the presence of noise in multi-class problems: Alleviating its influence with the one-vs-one decomposition
    • J.A.Sáez,, M.Galar, J.Luengo, and F.Herrera. 2014. Analyzing the presence of noise in multi-class problems: Alleviating its influence with the one-vs-one decomposition. Knowledge and Information Systems 38:179–206.
    • (2014) Knowledge and Information Systems , vol.38 , pp. 179-206
    • Sáez, J.A.1    Galar, M.2    Luengo, J.3    Herrera, F.4
  • 27
    • 84866043469 scopus 로고    scopus 로고
    • Predicting noise filtering efficacy with data complexity measures for nearest neighbor classification
    • J.A.Sáez,, J.Luengo, and F.Herrera. 2013. Predicting noise filtering efficacy with data complexity measures for nearest neighbor classification. Pattern Recognition 46:355–364.
    • (2013) Pattern Recognition , vol.46 , pp. 355-364
    • Sáez, J.A.1    Luengo, J.2    Herrera, F.3
  • 29
    • 0031164017 scopus 로고    scopus 로고
    • Prototype selection for the nearest neighbor rule through proximity graphs
    • J.Sánchez,, F.Pla, and F.Ferri. 1997. Prototype selection for the nearest neighbor rule through proximity graphs. Pattern Recognition Letters 18:507–513.
    • (1997) Pattern Recognition Letters , vol.18 , pp. 507-513
    • Sánchez, J.1    Pla, F.2    Ferri, F.3
  • 33
    • 0015361129 scopus 로고
    • Asymptotic properties of nearest neighbor rules using edited data
    • D.Wilson, 1972. Asymptotic properties of nearest neighbor rules using edited data. IEEE Transactions on Systems and Man and Cybernetics 2:408–421.
    • (1972) IEEE Transactions on Systems and Man and Cybernetics , vol.2 , pp. 408-421
    • Wilson, D.1
  • 35
    • 33847181085 scopus 로고    scopus 로고
    • In Proceedings of the 6th online world conference on soft computing in industrial applications
    • D.Wolpert, 2001. The supervised learning no-free-lunch theorems. In Proceedings of the 6th online world conference on soft computing in industrial applications, Springer London, 25–42.
    • (2001) The supervised learning no-free-lunch theorems , pp. 25-42
    • Wolpert, D.1
  • 37
    • 19544372918 scopus 로고    scopus 로고
    • Class noise vs. attribute noise: A quantitative study
    • X.Zhu,, and X.Wu. 2004. Class noise vs. attribute noise: A quantitative study. Artificial Intelligence Review 22:177–210.
    • (2004) Artificial Intelligence Review , vol.22 , pp. 177-210
    • Zhu, X.1    Wu, X.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.