Dealing with Data Difficulty Factors While Learning from Imbalanced Data
[ 1 ] Instytut Informatyki, Wydział Informatyki, Politechnika Poznańska | [ P ] employee
2016
chapter in monograph
english
EN Learning from imbalanced data is still one of challenging tasks in machine learning and data mining. We discuss the following data difficulty factors which deteriorate classification performance: decomposition of the minority class into rare sub-concepts, overlapping of classes and distinguishing different types of examples. New experimental studies showing the influence of these factors on classifiers are presented. The paper also includes critical discussions of methods for their identification in real world data. Finally, open research issues are stated.
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