Instance-Based Learning Algorithms
Machine Learning
An efficient algorithm for optimal pruning of decision trees
Artificial Intelligence
Pruning Algorithms for Rule Learning
Machine Learning
Separate-and-Conquer Rule Learning
Artificial Intelligence Review
Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Prediction algorithms and confidence measures based on algorithmic randomness theory
Theoretical Computer Science - Natural computing
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Feature Selection for Machine Learning: Comparing a Correlation-Based Filter Approach to the Wrapper
Proceedings of the Twelfth International Florida Artificial Intelligence Research Society Conference
Improving Classification by Removing or Relabeling Mislabeled Instances
ISMIS '02 Proceedings of the 13th International Symposium on Foundations of Intelligent Systems
Identifying and Handling Mislabelled Instances
Journal of Intelligent Information Systems
Data Mining
A penalized likelihood based pattern classification algorithm
Pattern Recognition
Patient-recognition data-mining model for BCG-plus interferon immunotherapy bladder cancer treatment
Computers in Biology and Medicine
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
SEPARATE: a machine learning method based on semi-global partitions
IEEE Transactions on Neural Networks
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A relabeling algorithm for retrieval of noisy instances with binary outcomes is presented. The relabeling algorithm iteratively retrieves, selects, and re-labels data instances (i.e., transforms a decision space) to improve prediction quality. It emphasizes knowledge generalization and confidence rather than classification accuracy. A confidence index incorporating classification accuracy, prediction error, impurities in the relabeled dataset, and cluster purities was designed. The proposed approach is illustrated with a binary outcome dataset and was successfully tested on the standard benchmark four UCI repository dataset as well as bladder cancer immunotherapy data. A subset of the most stable instances (i.e., 7% to 51% of the sample) with high confidence (i.e., between 64%-99.44%) was identified for each application along with most noisy instances. The domain experts and the extracted knowledge validated the relabeled instances and corresponding confidence indexes. The relabeling algorithm with some modifications can be applied to other medical, industrial, and service domains.