Iterative rule simplification for noise tolerant inductive learning
Peter W. Pachowicz, J. Bala, Jiaxing Zhang · 2003
An iterative noise reduction learning algorithm is presented in which rules are learned in two phases. The first phase improves the quality of training data through a concept-driven closed-loop filtration process. In the second phase, classification rules are relearned from the filtered training data set.>