A Wrapper for Reweighting Training Instances for Handling Imbalanced Data Sets Export

@incollection{citeulike:2165156, abstract = {A classifier induced from an imbalanced data set has a low error rate for the majority class and an undesirable error rate for the minority class. This paper firstly provides a systematic study on the various methodologies that have tried to handle this problem. Finally, it presents an experimental study of these methodologies with a proposed wrapper for reweighting training instances and it concludes that such a framework can be a more valuable solution to the problem.}, author = {Karagiannopoulos, M. and Anyfantis, D. and Kotsiantis, S. and Pintelas, P.}, citeulike-article-id = {2165156}, citeulike-linkout-0 = {http://dx.doi.org/10.1007/978-0-387-74161-1_4}, doi = {10.1007/978-0-387-74161-1_4}, journal = {Artificial Intelligence and Innovations 2007: from Theory to Applications}, keywords = {classification, learning}, pages = {29--36}, posted-at = {2007-12-25 08:28:50}, priority = {4}, title = {A Wrapper for Reweighting Training Instances for Handling Imbalanced Data Sets}, url = {http://dx.doi.org/10.1007/978-0-387-74161-1_4}, year = {2007} }

TY - CHAP ID - citeulike:2165156 L3 - citeulike-article-id:2165156 N2 - A classifier induced from an imbalanced data set has a low error rate for the majority class and an undesirable error rate for the minority class. This paper firstly provides a systematic study on the various methodologies that have tried to handle this problem. Finally, it presents an experimental study of these methodologies with a proposed wrapper for reweighting training instances and it concludes that such a framework can be a more valuable solution to the problem. JF - Artificial Intelligence and Innovations 2007: from Theory to Applications EP - 36 TI - A Wrapper for Reweighting Training Instances for Handling Imbalanced Data Sets SP - 29 KW - classification KW - learning AU - Karagiannopoulos, M AU - Anyfantis, D AU - Kotsiantis, S AU - Pintelas, P PY - 2007/// UR - http://dx.doi.org/10.1007/978-0-387-74161-1_4 ER -