Eliminating class noise in large datasets Export

@inproceedings{Zhu:2003, abstract = {This paper presents a new approach for identifying and eliminating mislabeled instances in large or distributed datasets. We first partition a dataset into subsets, each of which is small enough to be processed by an induction algorithm at one time. We construct good rules from each subset, and use the good rules to evaluate the whole dataset. For a given instance Ik, two error count variables are used to count the number of times it has been identified as noise by all subsets. The instance with higher error values will have a higher probability of being a mislabeled example. Two threshold schemes, majority and non-objection, are used to identify the noise. Experimental results and comparative studies from real-world datasets are reported to evaluate the effectiveness and efficiency of the proposed approach.}, author = {Zhu, Xingquan and Wu, Xindong and Chen, Qijun}, booktitle = {Proceedings of the Twentieth International Conference on Machine Learning (ICML-2003)}, citeulike-article-id = {5925687}, keywords = {classification, learning, phd, rules}, posted-at = {2009-10-12 10:33:07}, priority = {0}, title = {Eliminating class noise in large datasets}, year = {2003} }

TY - CONF ID - Zhu:2003 L3 - citeulike-article-id:5925687 N2 - This paper presents a new approach for identifying and eliminating mislabeled instances in large or distributed datasets. We first partition a dataset into subsets, each of which is small enough to be processed by an induction algorithm at one time. We construct good rules from each subset, and use the good rules to evaluate the whole dataset. For a given instance Ik, two error count variables are used to count the number of times it has been identified as noise by all subsets. The instance with higher error values will have a higher probability of being a mislabeled example. Two threshold schemes, majority and non-objection, are used to identify the noise. Experimental results and comparative studies from real-world datasets are reported to evaluate the effectiveness and efficiency of the proposed approach. TI - Eliminating class noise in large datasets T2 - Proceedings of the Twentieth International Conference on Machine Learning (ICML-2003) KW - classification KW - learning KW - phd KW - rules AU - Zhu, Xingquan AU - Wu, Xindong AU - Chen, Qijun PY - 2003/// ER -