An ensemble-driven k-NN approach to ill-posed classification problems
- 12 October 2005
- journal article
- research article
- Published by Elsevier BV in Pattern Recognition Letters
- Vol. 27 (4), 301-307
- https://doi.org/10.1016/j.patrec.2005.08.012
Abstract
No abstract availableKeywords
This publication has 13 references indexed in Scilit:
- Quality assessment of classification and cluster maps without ground truth knowledgeIEEE Transactions on Geoscience and Remote Sensing, 2005
- A Semilabeled-Sample-Driven Bagging Technique for Ill-Posed Classification ProblemsIEEE Geoscience and Remote Sensing Letters, 2005
- A Cost-Effective Semisupervised Classifier Approach With KernelsIEEE Transactions on Geoscience and Remote Sensing, 2004
- Learning Bayesian network classifiers for facial expression recognition both labeled and unlabeled dataPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2003
- Adaptive Bayesian contextual classification based on Markov random fieldsIEEE Transactions on Geoscience and Remote Sensing, 2002
- An Empirical Comparison of Voting Classification Algorithms: Bagging, Boosting, and VariantsMachine Learning, 1999
- Combining labeled and unlabeled data with co-trainingPublished by Association for Computing Machinery (ACM) ,1998
- Classification accuracy improvement of neural network classifiers by using unlabeled dataIEEE Transactions on Geoscience and Remote Sensing, 1998
- Bagging predictorsMachine Learning, 1996
- On the mean accuracy of statistical pattern recognizersIEEE Transactions on Information Theory, 1968