Noisy Data in Data Mining (2015)

2015 (8 papers)

  • Pasolli E, Melgani F. Genetic algorithm-based method for mitigating label noise issue in ECG signal classification. Biomedical Signal Processing and Control 2015;19:130-136.
  • Sáez J.A, Luengo J, Stefanowski J, Herrera F. SMOTE-IPF: Addressing the noisy and borderline examples problem in imbalanced classification by a re-sampling method with filtering. Information Sciences 2015;291:184-203.
  • Tomašev N, Buza K. Hubness-aware kNN classification of high-dimensional data in presence of label noise. Neurocomputing 2015;160:157-172.
  • Xu Y, Fang X, You J, Chen Y, Liu H. Noise-free representation based classification and face recognition experiments. Neurocomputing 2015;147(1):307-314.
  • Jin-Sheng L, Sun G, Zhang Q, Jun H. Similarity distance noise reduction of entropy based on lifting KNN classification performance. International Journal of Security and its Applications 2015;9(2):149-158.
  • Zhang Z, Wang L, Zhu Q, Liu Z, Chen Y. Noise modeling and representation based classification methods for face recognition. Neurocomputing 2015;148:420-429.
  • Jung H-W, Lee J-H. Noisy and incomplete fingerprint classification using local ridge distribution models. Pattern Recognition 2015;48(2):473-484.
  • Garcia, LPF, de Carvalho ACPLF, Lorena AC. Effect of label noise in the complexity of classification problems. Neurocomputing 2015,160:108-119.