Noisy Data in Data Mining (2013)

2013 (12 papers)

  • Frenay B, Doquire G, Verleysen M. Estimating mutual information for feature selection in the presence of label noise. Computational Statistics and Data Analysis 2013.
  • Wen G, Wei J, Wang J, Zhou T, Chen L. Cognitive gravitation model for classification on small noisy data. Neurocomputing 2013;118:245-52.
  • Sáez JA, Galar M, Luengo J, Herrera F. Tackling the problem of classification with noisy data using multiple classifier systems: Analysis of the performance and robustness. Inf Sci 2013;247:1-20.
  • Kylberg G, Sintorn I-. Evaluation of noise robustness for local binary pattern descriptors in texture classification. Eurasip Journal on Image and Video Processing 2013;2013.
  • Abellán J. An application of non-parametric predictive inference on multi-class classification high-level-noise problems. Expert Syst Appl 2013;40(11):4585-92.
  • Maani R, Kalra S, Yang Y-. Noise robust rotation invariant features for texture classification. Pattern Recognit 2013;46(8):2103-16.
  • An W, Liang M. Fuzzy support vector machine based on within-class scatter for classification problems with outliers or noises. Neurocomputing 2013;110:101-10.
  • Bootkrajang J, Kabán A. Classification of mislabelled microarrays using robust sparse logistic regression. Bioinformatics 2013;29(7):870-7.
  • Wu G-, Huang P-. A vectorization-optimization-method-based type-2 fuzzy neural network for noisy data classification. IEEE Trans Fuzzy Syst 2013;21(1):1-15.
  • Li H-, Yang J-, Zhang G, Fan B. Probabilistic support vector machines for classification of noise affected data. Inf Sci 2013;221:60-71.
  • Sáez JA, Luengo J, Herrera F. Predicting noise filtering efficacy with data complexity measures for nearest neighbor classification. Pattern Recognit 2013;46(1):355-64.
  • Lazzerini B, Volpi SL. Classifier ensembles to improve the robustness to noise of bearing fault diagnosis. Pattern Analysis and Applications 2013;16(2):235-51.