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Articles

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

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References

  • Azar, A. T., and A. E. Hassanien. 2015. Dimensionality reduction of medical big data using neural-fuzzy classifier. Soft Computing 19:1115–1127.
  • Brodley, C. E., and M. A. Friedl. 1999. Identifying mislabeled training data. Journal of Artificial Intelligence Research 11:131–167.
  • Cohen, W. W. 1995. Fast effective rule induction. In Proceedings of the twelfth international conference on machine learning. Morgan Kaufmann.
  • Devijver, P. 1986. On the editing rate of the MULTIEDIT algorithm. Pattern Recognition Letters 4:9–12.
  • Finner, H. 1993. On a monotonicity problem in step-down multiple test procedures. Journal of the American Statistical Association 88:920–923.
  • Frénay, B., and M. Verleysen. 2014. Classification in the presence of label noise: A survey. IEEE Transactions on Neural Networks and Learning Systems 25:845–869.
  • Gamberger, D., R. Boskovic, N. Lavrac, and C. Groselj. 1999. Experiments with noise filtering in a medical domain. In: Proceedings of the sixteenth international conference on machine learning. San Francisco, CA: Morgan Kaufmann.
  • Gamberger, D., N. Lavrac, and S. Dzeroski. 1996. Noise elimination in inductive concept learning: A case study in medical diagnosis. In Proceedings of the 7th international workshop on algorithmic learning theory. Berlin Heidelberg: Springer.
  • Gamberger, D., N. Lavrac, and S. Dzeroski. 2000. Noise detection and elimination in data preprocessing: Experiments in medical domains. Applied Artificial Intelligence 14:205–223.
  • Garcia, L. P. F., 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.
  • García, S., 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.
  • Hickey, R. J. 1996. Noise modelling and evaluating learning from examples. Artificial Intelligence 82:157–179.
  • Khoshgoftaar, T. M., and P. Rebours. 2007. Improving software quality prediction by noise filtering techniques. Journal of Computer Science and Technology 22:387–396.
  • Kononenko, I. 2001. Machine learning for medical diagnosis: History, state of the art and perspective. Artificial Intelligence in Medicine 23:89–109.
  • Krawczyk, B., 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.
  • Krawczyk, B., and G. Schaefer. 2014. A hybrid classifier committee for analysing asymmetry features in breast thermograms. Applied Soft Computing 20:112–118.
  • Krawczyk, B., 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.
  • le Cessie, S., and J. van Houwelingen. 1992. Ridge estimators in logistic regression. Applied Statistics 41:191–201.
  • Lichman, M. 2013. UCI machine learning repository. http://archive.ics.uci.edu/ml
  • Malossini, A., E. Blanzieri, and R. T. Ng. 2006. Detecting potential labeling errors in microarrays by data perturbation. Bioinformatics 22:2114–2121.
  • Mclachlan, G. J. 2004. Discriminant analysis and statistical pattern recognition, Wiley Series in Probability and Statistics. Wiley-Interscience.
  • Pombo, N., 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.
  • Quinlan, J. R. 1986. Induction of decision trees. Machine Learning 1:81–106.
  • Quinlan, J. R. 1993. C4.5: Programs for machine learning. San Francisco, CA, USA: Morgan Kaufmann.
  • Sáez, J. A., 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.
  • Sáez, J. A., 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.
  • Sáez, J. A., J. Luengo, and F. Herrera. 2013. Predicting noise filtering efficacy with data complexity measures for nearest neighbor classification. Pattern Recognition 46:355–364.
  • Sánchez, J., R. Barandela, A. Márques, R. Alejo, and J. Badenas. 2003. Analysis of new techniques to obtain quality training sets. Pattern Recognition Letters 24:1015–1022.
  • Sánchez, J., F. Pla, and F. Ferri. 1997. Prototype selection for the nearest neighbor rule through proximity graphs. Pattern Recognition Letters 18:507–513.
  • Teng, C. M. 1999. Correcting noisy data. In Proceedings of the sixteenth international conference on machine learning. San Francisco, CA, USA: Morgan Kaufmann.
  • Vapnik, V. 1998. Statistical learning theory. New York, NY, USA: Wiley.
  • Wang, R. Y., V. C. Storey, and C. P. Firth. 1995. A framework for analysis of data quality research. IEEE Transactions on Knowledge and Data Engineering 7:623–640.
  • Wilson, D. 1972. Asymptotic properties of nearest neighbor rules using edited data. IEEE Transactions on Systems and Man and Cybernetics 2:408–421.
  • Wilson, D. R., and T. R. Martinez. 1997. Improved heterogeneous distance functions. Journal of Artificial Intelligence Research 6:1–34.
  • Wolpert, D. 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.
  • Wu, X., and X. Zhu. 2008. Mining with noise knowledge: Error-aware data mining. IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans 38:917–932.
  • Zhu, X., and X. Wu. 2004. Class noise vs. attribute noise: A quantitative study. Artificial Intelligence Review 22:177–210.

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