Addressing the curse of imbalanced training sets: one-sided selection

@inproceedings{citeulike:2526066, abstract = {Adding examples of the majority class to the training set can have a detrimental effect on the learner's behavior: noisy or otherwise unreliable examples from the majority class can overwhelm the minority class. The paper discusses criteria to evaluate the utility of classifiers induced from such imbalanced training sets, gives explanation of the poor behavior of some learners under these circumstances, and suggests as a solution a simple technique called one-sided selection of...}, author = {Kubat, Miroslav and Matwin, Stan }, booktitle = {Proc. 14th International Conference on Machine Learning}, citeulike-article-id = {2526066}, keywords = {imbalanced, negative-example-selection}, pages = {179--186}, posted-at = {2008-06-04 15:39:16}, priority = {2}, publisher = {Morgan Kaufmann}, title = {Addressing the curse of imbalanced training sets: one-sided selection}, url = {http://citeseer.ist.psu.edu/kubat97addressing.html}, year = {1997} }

TY - CONF ID - citeulike:2526066 L3 - citeulike-article-id:2526066 TI - Addressing the curse of imbalanced training sets: one-sided selection T2 - Proc. 14th International Conference on Machine Learning SP - 179 EP - 186 PB - Morgan Kaufmann N2 - Adding examples of the majority class to the training set can have a detrimental effect on the learner's behavior: noisy or otherwise unreliable examples from the majority class can overwhelm the minority class. The paper discusses criteria to evaluate the utility of classifiers induced from such imbalanced training sets, gives explanation of the poor behavior of some learners under these circumstances, and suggests as a solution a simple technique called one-sided selection of... KW - imbalanced KW - negative-example-selection AU - Kubat, Miroslav AU - Matwin, Stan PY - 1997/// UR - http://citeseer.ist.psu.edu/kubat97addressing.html ER -