Learning decision rules in noisy domains
Proceedings of Expert Systems '86, The 6Th Annual Technical Conference on Research and development in expert systems III
Feature analysis for symbol recognition by elastic matching
IBM Journal of Research and Development
Instance-based prediction of real-valued attributes
Computational Intelligence
Incremental, instance-based learning of independent and graded concept descriptions
Proceedings of the sixth international workshop on Machine learning
Machine Learning
Machine Learning
Knowledge intensive exception spaces
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
K-D Decision Tree: An Accelerated and Memory Efficient Nearest Neighbor Classifier
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Two case studies in cost-sensitive concept acquisition
AAAI'90 Proceedings of the eighth National conference on Artificial intelligence - Volume 2
Analyses of instance-based learning algorithms
AAAI'91 Proceedings of the ninth National conference on Artificial intelligence - Volume 2
Cost-sensitive reinforcement learning for adaptive classification and control
AAAI'91 Proceedings of the ninth National conference on Artificial intelligence - Volume 2
Profiling instances in noise reduction
Knowledge-Based Systems
Classification of Unseen Examples under Uncertainty
Fundamenta Informaticae
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Several published reports show that instancebased learning algorithms yield high classification accuracies and have low storage requirements during supervised learning applications. However, these learning algorithms are highly sensitive to noisy training instances. This paper describes a simple extension of instancebased learning algorithms for detecting and removing noisy instances from concept descriptions. This extension requires evidence that saved instances be significantly good classifiers before it allows them to be used for subsequent classification tasks. We show that this extension's performance degrades more slowly in the presence of noise, improves classification accuracies, and further reduces storage requirements in several artificial and real-world database applications.