Early Drift Detection Method Export

@misc{citeulike:1215014, abstract = {An emerging problem in Data Streams is the detection of concept drift. This problem is aggravated when the drift is gradual over time. In this work we define a method for detecting concept drift, even in the case of slow gradual change. It is based on the estimated distribution of the distances between classification errors. The proposed method can be used with any learning algorithm in two ways: using it as a wrapper of a batch learning algorithm or implementing it inside an incremental ...}, author = {Manuel}, citeulike-article-id = {1215014}, citeulike-linkout-0 = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.61.6101}, keywords = {detection, drift}, posted-at = {2007-04-07 15:29:51}, priority = {2}, title = {Early Drift Detection Method}, url = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.61.6101} }

TY - GEN ID - citeulike:1215014 L3 - citeulike-article-id:1215014 N2 - An emerging problem in Data Streams is the detection of concept drift. This problem is aggravated when the drift is gradual over time. In this work we define a method for detecting concept drift, even in the case of slow gradual change. It is based on the estimated distribution of the distances between classification errors. The proposed method can be used with any learning algorithm in two ways: using it as a wrapper of a batch learning algorithm or implementing it inside an incremental ... TI - Early Drift Detection Method KW - detection KW - drift AU - Manuel UR - http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.61.6101 ER -