2014 (6 papers)

• Yang X, Tan L, He L. A robust least squares support vector machine for regression and classification with noise. Neurocomputing 2014;140:41-52.
• Triguero I, Sáez JA, Luengo J, García S, Herrera F. On the characterization of noise filters for self-training semi-supervised in nearest neighbor classification. Neurocomputing 2014;132:30-41.
• Seiffert C, Khoshgoftaar TM, Van Hulse J, Folleco A. An empirical study of the classification performance of learners on imbalanced and noisy software quality data. Information Sciences 2014;259:571-595.
• Wang Y, Wu G, Chen G, Chai T. Data mining based noise diagnosis and fuzzy filter design for image processing. Computers and Electrical Engineering 2014;40(7):2038-2049.
• Simon HU. PAC-learning in the presence of one-sided classification noise. Annals of Mathematics and Artificial Intelligence 2014;71(4):283-300.
• Frénay B, Verleysen M. Classification in the presence of label noise: A survey. IEEE Transactions on Neural Networks and Learning Systems 2014;25(5):845-869.
• Borut Sluban, Dragan Gamberger, Nada Lavrac. Ensemble-based noise detection: noise ranking and visual performance evaluation. Data Min Knowl Disc (2014) 28:265–303