Using Classifier diversity to handle label noise

No abstract is available for this paper.

• Publication year

  2015

• Venue

  IEEE International Joint Conference on Neural Network

• Publication date

  2015-07-12

• Fields of study

  Computer Science

• Identifiers
  DOI 10.1109/IJCNN.2015.7280316
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

• Classification in the Presence of Label Noise: A Survey

  2014cited by this paper
• Diversity measures for one-class classifier ensembles

  2014cited by this paper
• An Extensive Evaluation of Filtering Misclassified Instances in Supervised Classification Tasks

  2013cited by this paper
• Predicting noise filtering efficacy with data complexity measures for nearest neighbor classification

  2013cited by this paper
• DCPE co-training for classification

  2012cited by this paper
• A metric for unsupervised metalearning

  2011cited by this paper
• Improving classification accuracy by identifying and removing instances that should be misclassified

  2011cited by this paper
• Stability of Ranked Gene Lists in Large Microarray Analysis Studies

  2010cited by this paper
• Neural network with ensembles

  2010cited by this paper
• u NOISE DETECTION AND ELIMINATION IN DATA PREPROCESSING : EXPERIMENTS IN MEDICAL DOMAINS

  2010cited by this paper
• A study of the effect of different types of noise on the precision of supervised learning techniques

  2010cited by this paper
• The WEKA data mining software: an update

  2009cited by this paper
• KEEL: a software tool to assess evolutionary algorithms for data mining problems

  2008cited by this paper
• Class Noise Mitigation Through Instance Weighting

  2007influential reference
• Improving Software Quality Prediction by Noise Filtering Techniques

  2007cited by this paper
• Statistical Comparisons of Classifiers over Multiple Data Sets

  2006influential reference
• Estimating The Potential for Combining Learning Models

  2005cited by this paper
• Class Noise vs. Attribute Noise: A Quantitative Study

  2003cited by this paper
• Ensemble Methods for Noise Elimination in Classification Problems

  2003cited by this paper
• A noise filtering method using neural networks

  2003influential reference
• Estimating a Kernel Fisher Discriminant in the Presence of Label Noise

  2001cited by this paper
• On Discriminative vs. Generative Classifiers: A comparison of logistic regression and naive Bayes

  2001cited by this paper
• Multiple Classifier Systems

  2000cited by this paper
• Identifying Mislabeled Training Data

  1999cited by this paper
• Experiments with Noise Filtering in a Medical Domain

  1999cited by this paper
• A Reference Bayesian Test for Nested Hypotheses and its Relationship to the Schwarz Criterion

  1995cited by this paper
• Multivariate Decision Trees

  1995cited by this paper
• Programs for Machine Learning

  1994cited by this paper
• C4.5: Programs for Machine Learning

  1992cited by this paper
• An Experiment with the Edited Nearest-Neighbor Rule

  1976cited by this paper

• RFCL: A new under-sampling method of reducing the degree of imbalance and overlap

  2020influential citation
• Cartographie de l’occupation des sols à partir de séries temporelles d’images satellitaires à hautes résolutions Identification et traitement des données mal étiquetées

  2017influential citation