Corrective Classification: Learning From Data Imperfections With Aggressive And Diverse Classifier Ensembling

Publisher:
Pergamon-Elsevier Science Ltd
Publication Type:
Journal Article
Citation:
Information Systems, 2011, 36 (8), pp. 1135 - 1157
Issue Date:
2011-01
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Learning from imperfect (noisy) information sources is a challenging and reality issue for many data mining applications. Common practices include data quality enhancement by applying data preprocessing techniques or employing robust learning algorithms
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